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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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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Diseases

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Integrating Deep Learning with Case-based Reasoning for Enhanced Brain Tumor Detection: A Review 2

Integrating Deep Learning with Case-based Reasoning for Enha...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Krishna, Redrouthu Vamsi Dhatterwal, Jagjit Singh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350368413

Detecting brain tumors (BT) early is vital for effective treatment planning and improved patient outcomes. The fusion of Deep Learning (DL) and case-based reasoning (CBR) presents a promising solution for enhancing BT detection accuracy and reliability. DL methods, such as Convolutional Neural Networks (CNNs), excel in automatically extracting discriminative features from medical images, bolstering diagnostic capabilities. Case-based reasoning, on the other hand, leverages past diagnostic cases to provide context-aware decision support for new cases. This review study provides an overview of recent advancements in the integration of DL with CBR for brain tumor detection. Then discuss the strengths and limitations of both approaches and highlight how their integration can address key challenges in brain tumor detection, such as limited labeled data, interpretability of DL models, and the need for personalized diagnosis. Furthermore, review existing studies that have applied DLand CBR techniques individually and in combination for brain tumor detection. Finally, discuss challenges and future research directions and opportunities for leveraging this integrated approach to improve brain tumor diagnosis and patient care. In this, review total 55 papers are taken based on the Integrating DL with Case-Based Reasoning for Enhanced Brain Tumor Detection. In this, the research articles published between 2021 and early 2024 were taken into consideration for this study. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Approaches for Stroke Detection and SMOTE for Imbalanced data 8th

Machine Learning Approaches for Stroke Detection and SMOTE f...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Ruchitha, H.K. Sharma, B. S. Sanjana Sneha Rao, Trupthi Kodipalli, Ashwini Devamane, Shridhar B. Department of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bangalore India Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9789819976324

The majority of strokes will be caused by an unanticipated blockage of pathways by the heart and brain. To provide analytical data backing for timely, patient stroke prevention and detection, by creating a highly accurate and well-risk prediction model based on stroke risk factors using machine learning models (ML). We used numerous ML algorithms to analyse influencing factors to perform early detection, timely diagnosis, and timely treatment. In this paper, we have applied Decision Tree Classifier (DTC), K-Nearest Neighbours (KNN), and Logistic Regression (LR) for the analysis. The results were misleading because the original dataset had a data imbalance. To tackle this issue, the synthetic minority oversampling technique (SMOTE) was used in a sampling technique. There is also the use of k-fold cross-validation to extract more information about the performance of the algorithm and additionally comparing the accuracy of the three models. Therefore, this model can be used to support the decision-making of health care professionals. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Deep Masked Input UNet Framework based Lips Segmentation prediction for Gender Classification 2

Deep Masked Input UNet Framework based Lips Segmentation pre...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Devi, M. Shyamala Hepzhibha Rachel, S. Janani, G. Irene, B. Joy Praisy, E. Humaira, S. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Assessments have demonstrated that the human lip and its motions provide a wealth of knowledge about the identity and substance of communication. However, due to large differences in illumination condition, head perspective, and background, obtaining strong and precise lip image segmentation in natural settings remains difficult. This paper recommends Deep Masked Input UNet that categorizes the Gender based on the lip segmentation with high precision. For implementation, 10,132 face images from the KAGGLE Lip Segmentation dataset were used. The dataset includes 5066 images of the face and 5066 segmented lip images. The proposed Deep Masked Input UNet starts by masking the original picture with a segmented lip image to create masked face images with lips segments. Utilizing the Relu Activation function, Deep Masked Input UNet with contracting and expansion was enabled. The masked lip images are fitted to the proposed Deep Masked Input UNet and traditional CNN models. The results show that the suggested Deep Masked Input UNet model performs better in lip segmentation and gender classification, with a high accuracy of 98.95%. © 2024 IEEE.

关键词： Image segmentation

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A review: data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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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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BiGRU-ANN based hybrid architecture for intensified classification tasks with explainable AI

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4211-4221页

作者： Chakraborty, Sovon Talukder, Muhammad Borhan Uddin Hasan, Mohammad Mehadi Noor, Jannatun Uddin, Jia Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon South Korea

Artificial Intelligence (AI) is increasingly being employed in critical decision-making processes such as medical diagnosis, credit approval, criminal justice, and many more. However, many AI models exploit complex algorithms that are difficult for humans to see through, which can lead to concerns about accountability, bias, and the ability to trust the outcomes. With the increasing demand for AI systems to be transparent, interpretable, and reliable, the field of Explainable AI (XAI) has gained attention of the researchers. This paper presents a robust hybrid architecture that combines Bidirectional Gated Recurrent Units (BiGRU) and Artificial Neural Networks (ANN) for the classification of texts and sentiment analysis. Interpretable Model Agnostic Explanation (LIME) has been employed with our proposed model to enhance confidence in the outcomes. The proposed architecture is found to be effective for sentiment analysis from texts, and classifying images containing handwrit- ten characters. It leverages the BiGRU to model the sequential dependencies in the data, while the ANN is used for the final classification. Evaluations on both Bengali and English datasets show that the proposed architecture outperforms state-of-the-art models in various performance metrics, providing meaningful and interpretable explanations for its predictions. The model can be used in systems that require the architectures to be computationally less demanding, yet a decent accuracy is secured. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： ANN BiGRU Explainable AI LIME Sentiment analysis

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