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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sunaina Kaur, Sukhpreet Kaur, Sukhdeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Fungal infections at the skin of puppies, including dermatophytosis, are commonplace and pose great health dangers to each the animals and their proprietors. Although these infections do no longer affect human skin immediately, rapid detection remains important for well-timed remedy and spreading prevention. The currently established diagnosis process frequently includes the visual confirmation of the infection by medical professionals and fungal culture and is often referred to as time-intensive and highly dependent on medical professionals’ qualifications. Over the past few years, deep learning has become quite a promising area of research, R&D, and deployment in multiple fields, including medical imaging. The current research develops a deep learning model for fungal infection detection on the skin of dogs based on a variety of architectures and variations of Convolutional Neural Networks to classify image patches as healthy and infected. Transfer learning for the model structuring is reviewed to determine the impact of pre-trained models on the model’s learning efficiency. © 2025 the Author(s).

关键词： Convolutional neural networks

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A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

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computer Systems science & engineering 2023年第6期45卷 2553-2567页

作者： Anitha Thangasamy Bose Sundan Logeswari Govindaraj Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai-600025TamilndauIndia

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,*** Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber *** Denial of Service(DDoS)is the most common type of attack on cloud ***,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS *** DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS ***,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and *** this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD *** Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction *** deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM ***,it accurately predicts normal network traffic and detects anomalies resulting from DDoS *** proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

关键词： Cloud computing distributed denial of service particle swarm optimization long short-term memory attack detection

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Forecasting land use changes in crop classification and drought using remote sensing

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Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

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HMM-Based Secure Framework for Optical Fog Devices in the Optical Fog/Cloud Network

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Journal of Optical Communications 2023年第4期44卷 475-483页

作者： Sood, Sandeep K. Deep Singh, Kiran Department of Computer Science & Engineering Khalsa College of Engineering & Technology Amritsar India Department of Computer Science & Engineering Guru Nanak Dev University Regional Campus Punjab Gurdaspur India

In the present optical fog/cloud computing environment, optical line terminals and optical network units are used as the most promising optical fog devices (OFDs). The inherent characteristics of fog computing provide certain granted privileges to the attacker to hack devices and make them malicious. Also, existing security solutions generate false alarms that affect the performance of the underlying network. In this paper, a secure framework is proposed that not only predicts the malicious OFDs but also reduces false alarms. Hidden Markov model and intrusion detection system are used to detect the malicious one by computing the probability of shifting (POS) and then shift it to the virtual honeypot which is kept hidden by deploying it at the optical fog layer. In addition, it also reduces the generation of false alarm and logs all malicious activities for further analysis. In the experiment section, Python is used to validate the proposed framework. Further, HMM is simulated and tested in the MATLAB to reduce the false alarm rate. Results show that the proposed framework effectively reduces the false alarms and detects the malicious one and then shifts it onto the virtual honeypot efficiently. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fog

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A Survey on Enhancing Image Captioning with Advanced Strategies and Techniques

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computer Modeling in engineering & sciences 2025年第3期142卷 2247-2280页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Sajid Shah Mohammed ELAffendi School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning has seen significant research efforts over the last *** goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically *** real-world applications rely on image captioning,such as helping people with visual impairments to see their *** formulate a coherent and relevant textual description,computer vision techniques are utilized to comprehend the visual content within an image,followed by natural language processing *** approaches and models have been developed to deal with this multifaceted *** models prove to be stateof-the-art solutions in this *** work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption *** than reviewing all previous image captioning work,we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements,including encompassing image captioning with visual attention methods,exploring semantic information types in captions,and employing multi-caption generation ***,advancements such as neural architecture search,few-shot learning,multi-phase learning,and cross-modal embedding within image caption networks are examined for their transformative *** comprehensive quantitative analysis conducted in this study identifies cutting-edgemethodologies and sheds light on their profound impact,driving forward the forefront of image captioning technology.

关键词： Image captioning semantic attention multi-caption natural language processing visual attention methods

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Plastic Type Classification for Edge Devices Without Internet

Plastic Type Classification for Edge Devices Without Interne...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Sathyavani, M. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

The management of plastic waste in our ecosystem represents a significant environmental issue, necessitating effective sorting and categorization to facilitate efficient recycling practices. This study introduces a methodology utilizing deep learning for the automated identification of plastic varieties through the application of a Convolutional Neural Network (CNN) framework. The proposed method of adopting a pre-trained VGG16 to extract the features and tailors them with special layers in order to classify into seven categories that include HDPE, LDPE, Other, PET, PP, PS, and PVC. Data augmentation techniques were used to enhance the functionality of the model;image segmentation enhances object areas visibility. TensorFlow Lite was used to prepare the optimised model for its deployment to mobile platforms for real-time classifying different types of plastics on mobile devices. The optimization of mobile technology boosts the segregation of plastics at the origin, thereby allowing individuals to make informed choices in the recycling process and boosting recycling effectiveness through early differentiation. This approach generates great potential for alleviating environmental impacts through decentralized source-based plastic sorting and waste management. © 2025 IEEE.

关键词： Plastic recycling

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Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第4期36卷 481-497页

作者： R.Jayaraj S.Lokesh Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032TamilnaduIndia

Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant indexing *** the outcomes,it is dif-ﬁcult to discover data that the user can be absorbed ***,in order to determine the signiﬁcance of the data,it is important to identify the contents in an informative *** annotation can be one of the greatest problematic domains in multimedia research and computer ***,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image ***,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class *** pre-processed images are sent to the segmentation approach for efﬁcient labelling technique using J-image segmentation(JSEG).Theﬁnal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classiﬁer,the unlabeled images are *** proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_*** the assistance of the pro-posed methodology,the unlabeled images are labelled.

关键词： Deep learning model J-image segmentation honey badger algorithm convolutional neural network image annotation

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Enhancing COVID-19 and Pneumonia Detection with a Hybrid ACGAN-CNN Model 15

Enhancing COVID-19 and Pneumonia Detection with a Hybrid ACG...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sasipriyaa, N. Ravikumar, R. Sharmila, C. Gothai, E. Krishnakumar, B. Nithya Sri, R.K. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India The Erode College of Pharmacy Pharmaceutical Analysis Tamilnadu India SRM IST Trichy Department of Computer Science and Engineering India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350370249

Corona Virus caused a pandemic outbreak all over the world during 2020-2021. Identification of such diseases in the X-ray images needs help of deep learning methodologies involving classifiers. Achieving proficiency in training convolutional neural networks requires a considerable volume of data. Higher accuracy is needed while dealing with medical fieldrelated problems especially. When dealing with small datasets, networks tend to exhibit overfitting problems. In order to get larger dataset, techniques for data augmentation serve to enhance the neural networks' capacity for generalization by expanding existing training data more efficiently. While conventional data augmentation methods do enhance performance, their scope remains restricted. Generative Adversarial Networks (GANs) have proven instrumental in producing fresh data, thereby elevating the performance of CNNs. Methodologies for augmenting data with the help of GAN's is not as much notable in Convolutional neural networks. In this research study, a GAN architecture is used for augmenting X -ray images of chest in order to expand the dataset. Auxiliary Classifier Generative Adversarial Network model has been used to produce images. It consists of a generator that produces new images with random noise and a discriminator whose job is to identify fake & real images. The results showcase that the new image generated is fine-tuned over each epoch and the significance can be seen in the output images. We compare the quality of our ACGAN images with the conventionally augmented images. © 2024 IEEE.

关键词： COVID-19

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Feature engineering Methods for Analyzing Blood Samples for Early Diagnosis of Hepatitis Using Machine Learning Approaches

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computer Modeling in engineering & sciences 2025年第3期142卷 3229-3254页

作者： Mohamed A.G.Hazber Ebrahim Mohammed Senan Hezam Saud Alrashidi Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHail81481Saudi Arabia Department of Computer Science College of Applied SciencesHajjah UniversityHajjah9677Yemen Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyAl-Razi UniversitySana’a9671Yemen

Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to *** is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make *** study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data *** dataset contains missing values that have been processed and outliers *** dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)***,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA ***,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data *** RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training *** the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.

关键词： Hepatitis machine learning PCA RFE SelectKBest t-SNE

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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