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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Blaise, Don Bosco Manoj Kumar, G. Jeswanth, S. Bhuvaneswari, G. Rajesh, C. Bharathi, A. Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Skin cancer is a highly prevalent form of cancer that frequently arises from the proliferation of abnormal cells on the skin. Early detection is crucial for effective treatment and better prognosis. Traditional diagnostic methods face challenges in accuracy and timeliness, making automated techniques a valuable tool in disease identification. This article proposes a deep learning framework based on an ensemble, aimed at enhancing the detection and classification of various types of cancer on the skin. The approach integrates four optimized Convolutional Neural Networks (CNNs) using a weighted average ensemble technique. This model addresses common challenges in data preparation, model selection, and overfitting through comprehensive preprocessing and hyperparameter tuning, resulting in improved accuracy and robustness compared to existing systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Regularized Dense Layered LeNet towards Mushroom Type Classification 5

Regularized Dense Layered LeNet towards Mushroom Type Classi...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Devi, M. Shyamala Harivishnu, K.C. Suriya, Jaya V. Jayasuriya, K. Jason, Infant G.A. Farih, M.D. 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)9798350361155

Among the edible varieties of fungi, mushrooms possess the strongest nutrients found in the plant. Nevertheless, because of their great demand for food and significant benefits to medical research, it is imperative to identify the different varieties of mushrooms within the current species. The deep learning technology has recently rendering the CNN models towards classification of various object in interdisciplinary fields. This paper proposes Regularized Dense Layered LeNet (RDL-LeNet) that categorizes the mushroom types with high accuracy. The Mushrooms Classification dataset, which includes 3150 mushroom pictures was used for this execution. The Mushrooms Classification dataset was fitted with the existing CNN models to select the best CNN model. To determine the most efficient CNN framework, the Mushrooms Classification dataset was fitted using the available CNN models. When compared to the other CNN model, the LeNet performs better, with an accuracy rate of 90.67%. LeNet has now been chosen to adjust the quantity of dense layers. LeNet typically consist of three convolutions, two pooling layers, and two dense layers. In order to suggest the recommended framework RDL-LeNet, the LeNet is now changed by adding an additional dense layer that is made up of a single block of batch normalization, relu optimization, and drop out at the end that further compresses the output into vector size of 84. The RDL-LeNet is made up of three convolutional layers, two pooling layers, and three dense layers. The third dense layer consists of a single block that is made up of batch normalization, relu optimization, and dropout, ultimately leading to the final output layer. The suggested RDL-LeNet and the current CNN have been implemented with the Mushroom Classification dataset. The experiment demonstrates that the suggested RDL-LeNet model works better in the categorization of mushroom types, with a high accuracy of 98.68%. © 2024 IEEE.

关键词： Fungi

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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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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ReLU Activated Attention UNet Encipher Decipher Framework based Species Classification of Butterfly

ReLU Activated Attention UNet Encipher Decipher Framework ba...

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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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The most tenuous group query

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Frontiers of computer science 2023年第2期17卷 197-208页

作者： Na LI Huaijie ZHU Wenhao LU Ningning CUI Wei LIU Jian YIN Jianliang XU Wang-Chien LEE School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China Laboratory of Big Data Analysis and Processing Guangzhou 510006China School of Artificial Intelligence Sun Yat-Sen UniversityGuangzhou 510006China Department of Computer Science Anhui UniversityHefei 230601China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Department of Computer Science The Pennsylvania State UniversityState College 19019USA

Rtecently a lot of works have been investigating to find the tenuous groups,i.e.,groups with few social interactions and weak relationships among members,for reviewer selection and psycho-educational group ***,the metrics(e.g.,k-triangle,k-line,and k-tenuity)used to measure the tenuity,require a suitable k value to be specified which is difficult for users without background ***,in this paper we formulate the most tenuous group(MTG)query in terms of the group distance and average group distance of a group measuring the tenuity to eliminate the influence of parameter k on the tenuity of the *** address the MTG problem,we first propose an exact algorithm,namely MTGVDIS,which takes priority to selecting those vertices whose vertex distance is large,to generate the result group,and also utilizes effective filtering and pruning *** MTGVDIS is not fast enough,we design an efficient exact algorithm,called MTG-VDGE,which exploits the degree metric to sort the vertexes and proposes a new combination order,namely degree and reverse based branch and bound(DRBB).MTG-VDGE gives priority to those vertices with small *** a large p,we further develop an approximation algorithm,namely MTG-VDLT,which discards candidate attendees with high degree to reduce the number of vertices to be *** experimental results on real datasets manifest that the proposed algorithms outperform existing approaches on both efficiency and group tenuity.

关键词： tenuous group pruning strategy social network group query

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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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Medicinal Plant Classification Using SVM 2

Medicinal Plant Classification Using SVM

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kiranagi, Soumya S Patil, Savitha Sharnbasva University Department of Artificial Intelligence and Data Science Kalaburagi India Sharnbasva University Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350372892

there are hundreds of kinds of plants on Earth, and many of them have medicinal or curative properties. Approximately 80% of the global population continues to rely on traditional medicine. In Ayurveda, the use of herbs as medicine dates back to Vedic times. Recognizable identification of the medicinal plants is crucial for the efficacy of ayurvedic, social, and homoeopathic treatments. For the treatment of skin conditions, lung infections, asthma, dyspepsia, and other ailments, many people throughout the globe rely on medicinal plants. One of the most important steps in making an Ayurvedic medicine is finding the right medicinal leaf. Misclassification or incorrect identification of medicinal leaves could affect Ayurveda's efficacy. Form, colour, and texture of leaves are the most basic characteristics that must be present to identify a medicinal plant. While the form and venation pattern of individual leaves might vary greatly, members of the same class of leaves tend to have commonalities. In light of this need, here is a model that makes use of an SVM Machine Learning Algorithm to reliably identify and categories medicinal leaves. © 2024 IEEE.

关键词： Plant diseases

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AI-Powered Mental Health Screening and Support for Homeless Children

AI-Powered Mental Health Screening and Support for Homeless ...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Raga Madhuri, C.H. Bandaru, Jaya Sankar Krishna Srinu, Medisetti Vardhan, Gangadhari Midhun Anand Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798331536336

Mental health illness is a significant global public health threat exacerbated by the lack of effective early identification and intervention measures. This project aims to address these challenges by focusing on mental health recognition among homeless children. Individuals who are particularly vulnerable due to the loss of their parents. We created a model that screens mental health status by analyzing the responses to screening pediatric symptoms checklist (PSC) questions using an SVM algorithm. This model achieved 90% accuracy in predicting the mental health status of children. This approach makes it more accessible and effective for caretakers in orphan homes. The idea of providing some clear guidelines from the obtained results would make sure these children are well taken care of at the right time, which ultimately improves the mental health status of this group of people. © 2025 IEEE.

关键词： Pediatrics

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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