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Web service discovery with incorporation of web services clustering

International Journal of computers and Applications 2023年第1期45卷 51-62页

作者： Jalal, Sunita Yadav, Dharmendra Kumar Negi, Chetan Singh Department of Computer Engineering College of Technology GBPUAT Pantnagar India Department of Computer Science and Engineering MNNIT Allahabad Prayagraj India

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Web services

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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Diabetes Mellitus Prediction and Severity Calculation Using Stacked Ensemble Method

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SN computer science 2024年第8期5卷 1001页

作者： Ananthi, G. Santhiya, S. Gokila, V. Department of Computer Science and Engineering Mepco Schlenk Engineering College (Autonomous) Sivakasi India

Millions of individuals throughout the world suffer with diabetes mellitus, a chronic condition that can be effectively managed with early detection and precise prognosis. In this work, a machine learning method for predicting diabetes utilizes stacking ensemble algorithms with hyperparameter tuning. The proposed method uses three fundamental classifiers such as Random Forest, logistic regression, gradient boosting and tunes certain hyperparameters to improve the performance of each algorithm. Another classifier, called support vector machine, with hyperparameter tweaking is used as a meta-classifier to aggregate the predictions of the basic classifiers. It has been demonstrated that the stacking ensemble technique, especially when combined with hyperparameter tuning, enhances the prediction performance of machine learning models. The proposed technique is evaluated using two datasets, namely PIMA Indian Diabetes dataset, and dataset 2. Hierarchical clustering is used to forecast the disease's severity level. Severity levels are assigned based on the model's clustering results. The findings show that the suggested method accurately predicts diabetes and performs better than the individual base classifiers. To evaluate the severity level, a model created using a machine learning classification technique is chosen for further development. The model is finally included with the web application. The efficiency of the model is evaluated using a variety of performance criteria for evaluation such as accuracy, precision, recall, and F1-score. The proposed stacked ensemble method results in the accuracy of 97% which shows the improvement in the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accuracy Diabetes Severity Stacked ensemble method Web application

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Early Diabetics Prediction Using Multi Model Approaches in Machine Learning 9

Early Diabetics Prediction Using Multi Model Approaches in M...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Renugadevi, G. Sasi Kala Rani, K. Oswalt Manoj, S. Saranya, N. Goudhaman, M. Sri Krishna College of Engineering And Technology Department of Computer Science And Engineering Chennai India Dr Ngp Institute of Technology Department of Computer Science And Engineering Chennai India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

Diabetics is the maximum common non-transmissible disease and the deadliest disease worldwide, affecting five hundred and thirty-seven mountain individuals. Diabetics container be carried on by a variety of sources, with an unhealthy diet, high BP, abnormal cholesterol, a personal antiquity of the disease, and inactivity. Early diabetes prediction using IoT can be done by monitoring various health parameters and identifying patterns that may indicate the development of diabetes. In Proposed work, Mendeley data Repository is a real-time data have been taken for early diabetes prediction. The Proposed hybrid Backward elimination method with Maximum Relevance, Minimum Redundancy (BE-MRMR) algorithm When integrating backward elimination and the MRMR algorithm, you can use the strengths of both strategies to identify a small but informative set of features for early diabetic prediction, potentially enhancing model interpretability and generalisation. Finally, after eliminating features, left with a subdivision of structures that subsidize most to the replica's performance. This feature given to various machine learning classifiers RF, ANN, SVM to predict early diabetics. These models produce performance matrices is calculated in terms of accuracy, specificity, precision and sensitivity. © 2024 IEEE.

关键词： Forecasting

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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Realtime Object Detection Through M-ResNet in Video Surveillance System

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Intelligent Automation & Soft Computing 2023年第2期35卷 2257-2271页

作者： S.Prabu J.M.Gnanasekar Department of Computer Science&Engineering Karpaga Vinayaga College of Engineering and TechnologyChengalpattuTamilnadu603308India Department of Computer Science&Engineering Sri Venkateshwara College of Engineering&TechnologyChennai602001TamilnaduIndia

Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as trafﬁc signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difﬁculty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,ﬁrst we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modiﬁed ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufﬁcient *** results provide the comparison of existing method output and modiﬁcation architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andﬁnds a reasonable improvement in the object detection.

关键词： Object detection ResNet video survilence image processing object quality

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Enhancing Deep Learning Models for Sentiment Analysis Integrating Texts and Emojis: A Comprehensive Survey 10

Enhancing Deep Learning Models for Sentiment Analysis Integr...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Suresh Kumar, S. Gayathri, D. Shugavaneshwar, R. Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350353068

Sentiment analysis is used to get meaningful insights from data that is being retrieved from various resources or social media platforms. Sentiment analysis plays an important role in making crucial decisions that can improve the existing status significantly. This survey addresses and compares various machine learning models and deep learning models that have been recently employed for efficient sentiment analysis. In general, people use both texts and emojis to express their emotions. Considering the above fact, the proposed system addresses reviews that integrate both texts and emojis. The convolution neural network (CNN) is trained with a dataset that contains a cluster of texts and emojis. However, the texts and emojis are handled separately by processing them independently for efficient results. © 2024 IEEE.

关键词： Sentiment analysis

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Autoencoder-Based Architecture for Identification and Mitigating Phishing URL Attack in IoT Using DNN

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Journal of The Institution of Engineers (India): Series B 2023年第6期104卷 1227-1240页

作者： Gopal, S.B. Poongodi, C. Nanthiya, D. Kirubakaran, T. Kulavishnusaravanan, B. Logeshwar, D. Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal India Department of Computer Technology-UG Kongu Engineering College Perundurai India

As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials science and engineering, vol. 1055, no. 1, p. 012072. IOP Publishing). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score. © 2023, The Institution of Engineers (India).

关键词： Deep neural networks

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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IoMT & ML Model to Detect Stress in Human 2

IoMT & ML Model to Detect Stress in Human

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Nadh Singh, Bhaludra R Ghanta, Sindhura Saishivani, Thokala Vaishnavi, Basingala Sai Sravya, Polishetty Shifa, Shaik Bhoj Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Hyderabad India

ISBN: (纸本)9798350364699

Today's fast-moving surroundings cause stress for heaps. Real-time stress administration may be troublesome, regardless of the allure of negative effects on tangible and insane fitness. ML, IoMT, and AI abilities change how we live and work and solve this question. "A Smart Biomedical Healthcare System to Detect Stress Utilizing IoT, Machine Learning and Artificial Intelligence,"intends to utilize physiological sensors like soul rate, hotness, dampness (sweat) sensors and first verbalizations to detect stress. The submitted approach can path and categories folk into "difficult"and "non-tense"circumstances. Our classification results manifest the advantage of our approach in certain-occasion stress identification. The main aim of the original search out supports a novel procedure of identifying early stress something did surroundings, knowledge atmospheres, and administrative settings to support laborers' and scholars' welfare and someday increase productivity. © 2024 IEEE.

关键词： mHealth

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