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Multi-class Classification of Class Imbalanced Skin Lesion Dataset Using a Modified SMOTE-ENN Gabor-Enhanced VGG-19 Architecture

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SN computer science 2025年第3期6卷 1-17页

作者： Sahoo, Madhusmita Priyadarshini Sridhar, Rajeswari Computer Science and Engineering National Institute Technology Tamilnadu Tiruchirappalli 620015 India

The persistent rise in skin cancer cases has resulted in a high mortality rate of the affected patients due to late detection. In this paper, we proposed a sophisticated deep-learning model MSGNet for the automatic multiclass categorization of skin lesions to address the issues in the multiclass dataset. This work is subdivided into three components. First, in the preprocessing layer, a modified version of SMOTE-ENN is proposed, which uses radius neighbor-based grouping of class elements, an improved data augmentation technique for effectively detecting minority classes in a multiclass environment. Second, in the feature extraction layer, we created a modified VGG19 model which contains parallel Gabor-based convolutional layers in the initial layers. The augmented data is then fed to the parallel convolution layers which capture various extract discriminative features from the same input image. In the third step, in the classification layer, the last layer of modified VGG19 is replaced with two more fully connected layers and one softmax layer to classify skin lesion images. We considered the HAM 10000 and ISIC 2019 datasets in our experiment. MSGNet significantly improved the detection in almost all the classes, and the overall accuracy was found to be 89.33% and 90.62% for the HAM 10000 and ISIC 2019 datasets respectively. Also, we observed significant drift in the F1 score of individual classes which demonstrates that our modified approach efficiently distinguishes each class. It has been experimented that MSGNet performed better than some existing works, and offers a creative and workable way to automate and speed up the task of classifying skin lesions while saving effort, time, and human lives. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class-imbalance handling Gabor convolution Multi-class Parallel convolution layer Skin lesion classification SMOTE-ENN VGG-19

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A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness

Health Care Science

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Health Care science 2024年第6期3卷 409-425页

作者： Somit Jain Shobhit Agrawal Eshaan Mohapatra Kathiravan Srinivasan School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia

Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies *** research article conducts a time series analysis of COVID-19 data across various countries,including India,Brazil,Russia,and the United States,with a particular emphasis on total confirmed ***:The proposed approach combines auto-regressive integrated moving average(ARIMA)'s ability to capture linear trends and seasonality with long short-term memory(LSTM)networks,which are designed to learn complex nonlinear dependencies in the *** hybrid approach surpasses both individual models and existing ARIMA-artificial neural network(ANN)hybrids,which often struggle with highly nonlinear time series like COVID-19 *** integrating ARIMA and LSTM,the model aims to achieve superior forecasting accuracy compared to baseline models,including ARIMA,Gated Recurrent Unit(GRU),LSTM,and ***:The hybrid ARIMA-LSTM model outperformed the benchmark models,achieving a mean absolute percentage error(MAPE)score of 2.4%.Among the benchmark models,GRU performed the best with a MAPE score of 2.9%,followed by LSTM with a score of 3.6%.Conclusions:The proposed ARIMA-LSTM hybrid model outperforms ARIMA,GRU,LSTM,Prophet,and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE,symmetric mean absolute percentage error,and median absolute percentage error across all countries *** findings have the potential to significantly improve preparedness and response efforts by public health authorities,allowing for more efficient resource allocation and targeted interventions.

关键词： COVID-19 time series ARIMA LSTM GRU prophet ANN proposed model

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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

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工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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Triple Stack Deep Variational Autoencoder For Improved Hand Gesture Recognition 15

Triple Stack Deep Variational Autoencoder For Improved Hand ...

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

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra National Institute of Technology Sikkim Computer Science and Engineering Sikkim India National Institute of Technology Jamshedpur Computer Science and Engineering Jharkhand India

ISBN: (纸本)9798350370249

This paper proposes a novel approach for hand gesture recognition using a triple-stack deep variational autoencoder. By employing a VAE framework, we facilitate both efficient representation learning and the generation of meaningful latent spaces for gesture recognition and classification. This is an extension of the traditional VAE architecture that incorporates three layers of encoding and decoding with a spatial deep neural network. In a regular VAE, there is typically only one layer for both the encoder and decoder. By stacking three layers in both the encoder and decoder, a triple-stack deep VAE can learn more complex hierarchical representations of the input data. Each layer in the encoder extracts increasingly abstract features from the input, while each layer in the decoder reconstructs the input from these abstract representations. The performance of the proposed model is evaluated in terms of accuracy, precision, recall, and F1-score on six benchmark datasets: ASL Static, Massey University Dataset (MUGD), ASL Digit, NUS-2, Bengali Sign Language (BSL), and Hagrid-14 (HG-14) datasets. The results of the experiment show that the proposed 3S-DVE achieves an accuracy of 76% (MUGD Set 1), 80% (MUGD Set 2), 97% (MUGD Set 3), 96% (MUGD Set 4), 86% (MUGD Set 5), 97% (ASL Digit), 62% (ASL Static), 74% (NUS-II Dataset), 66% (BSL), 76% (MUGD Set 1), 80% (MUGD Set 2), 97% (MUGD Set 3), 96% (MUGD Set 4), 86% (MUGD Set 5), 97% (ASL Digit), 62% (ASL Static), 74% (NUS-II Dataset), 66% (BSL), and 79% (HG-14), respectively, which is better compared to the state-of-the-art methods. © 2024 IEEE.

关键词： Decoding

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Enhancing skin lesion diagnosis with data augmentation techniques: a review of the state-of-the-art

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Multimedia Tools and Applications 2024年 1-40页

作者： Patil, Aniket Mehto, Anjula Nalband, Saif Maharashtra Pune411048 India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Bhadson Road Punjab Patiala147004 India

Even if more and more high-quality public datasets are available, one of the biggest problems with deep learning for skin lesion diagnosis is the scarcity of training samples. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable performance across multiple computer Vision tasks. Despite their tendency to surpass conventional machine learning methods, the models produced by these algorithms require larger datasets to be accessible during training. An increasingly popular method to deal with this problem is data augmentation, especially in domains like medical imaging where big datasets are hard to come by. Experiments on independent, unknown datasets have demonstrated improved performance using data augmentation, which creates more data to train the model. This survey devotes a lot of time to using augmentation techniques based on basic data augmentation algorithms and the pros and cons of Test Time Augmentation (TTA), Generative Adversarial Networks (GANs), Variational Auto-Encoders (VAEs), and Synthetic Minority Oversampling Techniques (SMOTE). The main focus of this survey is data augmentation, which addresses the problem of limited data by providing a solution within the data space. When these approaches are incorporated into clinical practice, our research attempts to clarify them and increase trust in the reliability of AI models created from augmented data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

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computer Systems science & engineering 2023年第3期44卷 2439-2456页

作者： C.P.Thamil Selvi R.PushpaLaksmi Department of Computer science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and Technology PSNA College of Engineering and TechnologyDindigulTamilnadu624622India

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

关键词： Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification

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Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

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computer Systems science & engineering 2023年第4期45卷 701-714页

作者： Helen Josephine V L Ramchand Vedaiyan V.M.Arul Xavier Joy Winston J A.Jegatheesan D.Lakshmi Joshua Samuel Raj Business Analytics School of Business and ManagementChrist UniversityBangalore560029India Department of Computer Science and IT School of SciencesJain Deemed to be UniversityBangalore560027India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India College of Computer Studies University of Technology Bahrain18041Bahrain Institute of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123India Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India

The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum *** essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further *** dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the *** paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy *** proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy *** accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy ***,for improving the performance of the SOC estimation process,the HBFOA is *** addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall *** ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several *** experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

关键词： Internet of things energy systems state of charge estimation machine learning deep learning metaheuristics

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Deep learning based predicting urban traffic congestion with RGB-coded images using GRU-CNN and LSTM

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Multimedia Tools and Applications 2024年第38期83卷 86261-86280页

作者： P, Rajesh Azhagiri, M. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600 089 India

Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GRU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a RGB coded image which is the input to the GRU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Pseudo Random Number Generator Based on Cellular Automata with Self Organized Criticality

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SN computer science 2024年第5期5卷 454页

作者： Poornima, I. Gethzi Ahila Yogaraja, C.A. Venkatesh, R. Sudha, M. Swarna Vijayalakshmi, B. Computer Science and Engineering Ramco Institute of Technology Tamilnadu Rajapalayam India

In cryptography, pseudo-random numbers are crucial. The degree of strength of a cryptographic technique is directly influenced by the key’s randomness. Although numerous researchers have shown that cellular automata (CA) are effective as pseudo-random number generators (PRNGs), the fundamental CA structure is being changed to increase the efficiency in terms of randomness. Here, Langton's Ant Model and Programmable Controllable Cellular Automata (PCCA) in combination with a sand pile are proposed. Sand pile model and Langton’s Ant model are built for the generation of Rule control word for the successive iterations and Cell control word in selecting controllable cells for iterations respectively. The Controllable Cellular Automaton types used here are CCA4 and CCA5. Both Sand pile model and Langton’s Ant model exhibit chaotic behaviour. Two different rule sets are used and their results are compared. Also the frequency of each rule in the rule set are determined and found to be equally utilized. Due to the integration of these systems with cellular automata, which likewise display dynamic chaotic behaviour. Its results are greater efficiency in terms of randomness. Pseudo Random number sequence generated by this proposed PCCA is benchmarked with the statistical NIST test. The experiment shows that the resulting random sequence is highly random. Their entropies are calculated and the respective graphs are plotted. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cellular automata (CA) Cryptography Programmable Controllable Cellular Automata (PCCA) Pseudo Random number Pseudo-Random Number Generators (PRNGs)

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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