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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Devi, M. Shyamala Jeeva, R. Jagadheeswaran, R. Kumar V, Hemand Erwin Nicholas, M. Keerthi Vasan, 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)9798350363289

The recognition and categorization of butterflies is crucial for the preservation of butterfly species in the fields of entomology, computer vision and deep learning. Environmentalists have long utilized butterflies as model organisms in the research on the effects of habitat degradation, dispersion, and warming temperatures. This paper recommends Relu Activated Attention UNet (RAA-UNet) that categorizes the nine butterfly species with high precision. The Butterfly dataset contains 1664 butterfly images that were used in this investigation. The dataset consists of 832 photos of butterflies and 832 related images that have been segmented. The proposed RAA-UNet starts by creating masked butterfly pictures by masking the original image with a segmented butterfly image. To classify the butterfly species, the masked butterfly pictures are assigned to Attention UNet with Decoder and Encoder combined with the Attention gate and activated using Relu Activation function. The proposed RAA-UNet and conventional CNN models are fitted to the masked butterfly pictures. As demonstrated by the experiments, the RAA-UNet model works better with a high accuracy of 97.75% in the classification of butterfly species. © 2024 IEEE.

关键词： Invertebrates

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Normalized AlexNet Deep Learning based Edibility Classification of Sporocarp 2

Normalized AlexNet Deep Learning based Edibility Classificat...

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

作者： Shyamala Devi, M. Irene, B. Kanagavalli, A. Kousika, A. Joy Praisy, E. Humaira, S. 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

The manufacturing and dissemination of spores is the main purpose of sporocarps which is the specialized type of configuration found in freshwater plants of the Salviniales family. Though sporocarp are mainly used for reproduction, some of them are consumable by the animals and birds for the extraction of rich nutrients. Sporocarp categorization is an extremely difficult task as various species of sporocarp share significant appearance. Some sporocarp may be poisonous that lead to brain damage and infection to the animals. This paper proposes Normalized AlexNet that classifies the edible and poisonous sporocarps with high efficiency. For the sake of this investigation, 1800 sporocarp images from the Sporocarp edible dataset were used. The best CNN model was chosen by applying the Sporocarp edible dataset to the current CNN models. With an accuracy of 89.67%, AlexNet performs better than the other CNN models. AlexNet has been chosen to fine-tune its single dense layer at this level. The AlexNet typically consists of two blocks with a single convolution including a max pooling layer, one block with three convolutions along with a max pooling layer, and one last block with three dense layers. The proposed Normalized AlexNet was now suggested by modifying the second dense layer with a single block of batch normalization, relu optimization, and drop out with a vector size of 4096. The suggested Normalized AlexNet and the current CNN have both been fitted with the Sporocarp Edible dataset. The demonstration illustrates that the Normalized AlexNet model performs better in the edibility categorization of sporocarp, with a high accuracy of 98.76%. © 2024 IEEE.

关键词： Convolution

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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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Auxiliary Dense Layered LeNet Deep Learning based Mango Variety Classification 2

Auxiliary Dense Layered LeNet Deep Learning based Mango Vari...

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

作者： Shyamala Devi, M. Janani, G. Fernandes, Jonita Maria Harshini, T. Nivetha, R. Joshika Rao, O.R. 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

In the agriculture sector, physical classification of fruits is a costly process that can produce inconsistent outcomes due to human negligence. Fruit categorization from snapshots is an extremely difficult venture, especially for mango fruits because various species of mango fruits share significant resemblance. This paper proposes Auxiliary Dense Layered LeNet (ADL-LeNet) that classifies the mango type with high accuracy. The Mango Varieties Classification and Grading dataset, which includes 1200 mango pictures was used for this work. The Mango image dataset is applied with the existing CNN model to select the best CNN model. The LeNet outperforms with the accuracy of 92.77% when compared to the other CNN model. Now LeNet is selected for finetuning the number of dense layers. Generally, the LeNet is composed of three convolutions, two subsampling followed by two dense layers. Now, the LeNet is modified by adding one extra dense layer at the end that compress the output further into vector size of 42 to suggest the proposed model ADL-LeNet. The ADL-LeNet composed of three convolutional, two subsampling and three dense layers ending with the final output layer. The Mango Image dataset has been applied with the existing CNN and proposed ADL-LeNet. The implementation shows that the proposed ADL-LeNet model outperforms with the high accuracy of 97.6% in mango type classification. © 2024 IEEE.

关键词： Fruits

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Drug Recommendation based on drug details and Optimization using Bee Algorithm

Drug Recommendation based on drug details and Optimization u...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Ariyachaipong, Kornpong Senaluang, Pattayapon Songmuang, Pokpong Kongkachandra, Rachada Thammasat Universtiy Data Science And Innovation College Of Interdisciplinary Studies Pathum Thani Thailand Thammasat Universtiy Department Of Computer Science Bangkok Thailand Thammasat Universtiy Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand

ISBN: (纸本)9798350370096

In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aims to address the challenge of medication selection in resource-constrained settings by providing physicians with informed recommendations tailored to patient *** proposed system utilizes drug data sourced from ***, encompassing approximately 34,284 drug entries. To ensure high-quality recommendations, the data undergoes a rigorous preprocessing phase involving null value imputation and feature extraction using techniques like TF-IDF. Following preprocessing, a combination of Drug Recommendation with cosine similarity and the Bee Algorithm is employed. Cosine similarity establishes a baseline for drug similarity, while the Bee Algorithm optimizes the selection process by considering additional factors beyond just similarity, such as potential side effects and cost-effectiveness. © 2024 IEEE.

关键词： Recommender systems

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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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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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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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MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1551-1567页

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciaran University of Limerick Department Electronic and Computer Engineering LimerickV94 T9PX Ireland University of Limerick SFI CRT Foundations in Data Science Ireland Research Centre Ireland

Accurate detection and trajectory prediction of moving vehicles are essential for motion planning in autonomous driving systems. While traffic regulations provide clear boundaries, real-world scenarios remain unpredictable due to the complex interactions between vehicles. This challenge has driven significant interest in learning-based approaches for trajectory prediction. We present MMTraP: Multi-Sensor and Multi-Agent Trajectory Prediction in BEV. This method integrates camera, LiDAR, and radar data to create detailed Bird's-Eye-View representations of driving scenes. Our approach employs a hierarchical vector transformer architecture that first detects and classifies vehicle motion patterns before predicting future trajectories through spatiotemporal relationship modeling. This work specifically focuses on vehicle interactions and environmental constraints. Despite its significance, multi-agent trajectory prediction and moving object segmentation are still underexplored in the literature, especially in real-time applications. Our method leverages multisensor fusion to obtain precise BEV representations and predict vehicle trajectories. Our multi-sensor fusion approach achieves the highest vehicle Intersection over Union (IoU) of 63.23% and an overall mean IoU (mIoU) of 64.63%, demonstrating its effectiveness in utilizing all available sensor modalities. Additionally, we demonstrate vehicle segmentation and trajectory prediction capabilities across various lighting and weather conditions. The proposed approach has been rigorously evaluated using the nuScenes dataset. Results show that our method improves the accuracy of trajectory predictions and outperforms state-of-the-art techniques, particularly in challenging environments such as congested urban areas. For instance, in complex traffic scenarios, our approach achieves a relative improvement of 5% in trajectory prediction accuracy compared to baseline methods.. This work advances vehicle-focused prediction syst

关键词： Semantic Segmentation

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