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6th International Conference on computing Methodologies and Communication, ICCMC 2022

作者： Bhagya, A. Sripriya, P. Vels Institute of Science Technology Advanced Studies Department of Computer Applications Tamilnadu Chennai India

ISBN: (纸本)9781665410281

The educational sector generates a large amount of data on a daily basis because of the rapid growth of data generation an educational system is facing difficulties in predicting students' performance which is an essential for education institutions and existing methods or not satisfactory for predicting students' performance because of large data sets. As educational institutions looking for an efficient and advanced technology which predicts student's performance, big data and predictive learning analysis is an emerging trend in educational system to improve students' academic learning and growth of the institution in today's competitive world. This paper focus on a literature review on an associated benefits and challenges by implementing predictive learning analytics and brief information about different predictive analytics techniques which can be implemented in education system to gain meaningful insights into available data and to assist the education system to improve their growth by monitoring the students closely based on the predicted data. © 2022 IEEE.

关键词： Predictive analytics

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Advances in Machine Learning for Stroke Prediction: A Comprehensive Review

Advances in Machine Learning for Stroke Prediction: A Compre...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： F. Mohammed Arshad K. Sharmila Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The comparative study in this review paper covered several phases of predictive modelling and examining the efficacy of different models for stroke prediction. Thorough exploratory data analysis (EDA) and strict data pretreatment were part of the initial look into, which made sure the model building process had a strong foundation. A thorough examination of the prediction performance of a various machine learning algorithms was made possible by the analysis, which included everything from traditional classifiers to state-of-the-art ensemble techniques. Lazy Predictor was used for benchmarking and initial model selection, which made it simple to identify the best-performing algorithms. These models' predictive accuracy was further improved by subsequent fine-tuning, which improved their appropriateness for stroke prediction tasks. In addition, the investigation of ensemble learning strategies cleared the way for the creation of hybrid models, which combine the advantages of distinct classifiers to produce better prediction results. The comparison analysis yielded useful insights that influence the selection of classification models for stroke prediction by healthcare researchers and practitioners. Stakeholders can embrace and apply ML approaches for stroke prediction with more knowledge if the study's findings are summarized. This will lead to improved medical outcomes and therapy.

关键词： Analytical models Accuracy Machine learning algorithms Reviews Soft sensors Predictive models Prediction algorithms Data models Reliability Stakeholders

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Multimodal Learning in Image Processing

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computers, Materials & Continua 2025年第2期82卷 3615-3618页

作者： Zhixin Chen Gautam Srivastava Shuai Liu School of Educational Science Hunan Normal UniversityChangsha410081China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha410081China Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung406040Taiwan Institute of Engineering and Technology Chitkara UniversityChandigarh140401India

1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.

关键词： Image computer LSTM

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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SVM-RideNN: hybrid support vector machine-rider neural network based sentiment analysis using product review

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Australian Journal of Electrical and Electronics Engineering 2025年

作者： A S, Sridhar Nagasundaram, S. Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

Nowadays, shopping on online sites has become most mainstream for users. Moreover, the social media comments and reviews serve a huge role in online shopping. The satisfactions of users have also been improved with the utilization of sentiment analysis (SA). In addition, the sentiment analysis determines the digital text, in which the messages are positive, negative, or neutral. SA has difficulty in examining sentiments that are expressed in different languages, dialects, or colloquialisms. Moreover, it faces trouble with slang, abbreviations, and emojis. To bridge this gap, a hybrid Support Vector Machine Rider Optimization Neural Network (SVM-RideNN)-based sentimental analysis is proposed in this research. This method aims to examine the type and identification of text representation, and performance of sentiment analysis. The input product reviews are obtained from the acquired dataset, and the essential features are refined. From the refined features, the sentimental analysis process is done using the SVM-RideNN. In addition, the metrics, namely precision, recall, F1-score, and rouge, are employed to validate the performance of the model, and it has attained the finest output of 91.8%, 94.9%, 93.3%, and 86.9%. © 2025 Engineers Australia.

关键词： Support vector machines

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Improved Segmentation of Neurodegenerative Disease using Deep Auto Encoders

Improved Segmentation of Neurodegenerative Disease using Dee...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： B. Mahalakshmi A. Thirumurthi Raja Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai

Previous research has demonstrated that there is a correlation between a person body mass index (BMI) and their brain volume, and that correlation is typically an inverse one. However, the Body Mass Index (BMI), which does not take into account the fact that different areas of fat carry varying degrees of metabolic risk, is not necessarily the best indicator of health. This is because it does not consider the fact that different areas of fat carry different degrees of risk. In order to determine whether or whether there is a connection between MR-based fat depots and the quantity of gray matter (GM) in the brain, which is known to decrease in individuals, who suffer from neurodegenerative disorders. In this paper, Deep Auto Encoders (DAE) are used as a segmentation model to segment the neurodegenerative diseases in patients. Initially, the images are collected from the database; it is then pre-processed and segmented using DAE. The simulation is carried out in python to test the efficacy of unsupervised DAE segmentation over various conventional segmentation models. The results of simulation shows that the proposed method achieves higher rate of accuracy than other methods.

关键词： Image segmentation Correlation Databases Feature extraction Brain modeling Fats Indexes

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective

Cross-domain NER with Generated Task-Oriented Knowledge: An ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Zhihao Lee, Sophia Yat Mei Wu, Junshuang Zhang, Dong Li, Shoushan Cambria, Erik Zhou, Guodong School of Computer Science & Technology NLP Lab Soochow University China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong Beijing Jinghang Research Institute of Computing and Communication China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP), enabling learning from source to target domains with limited data. Previous studies often rely on manually collected entity-relevant sentences from the web or attempt to bridge the gap between tokens and entity labels across domains. These approaches are time-consuming and inefficient, as these data are often weakly correlated with the target task and require extensive pre-training. To address these issues, we propose automatically generating task-oriented knowledge (GTOK) using large language models (LLMs), focusing on the reasoning process of entity extraction. Then, we employ task-oriented pre-training (TOPT) to facilitate domain adaptation. Additionally, current cross-domain NER methods often lack explicit explanations for their effectiveness. Therefore, we introduce the concept of information density to better evaluate the model's effectiveness before performing entity recognition. We conduct systematic experiments and analyses to demonstrate the effectiveness of our proposed approach and the validity of using information density for model evaluation. © 2024 Association for Computational Linguistics.

关键词： Domain Knowledge

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An Ensemble Deep Learning and Modified Wild Horse Optimization based Pixel Selection for Secured Content-based Medical Image Retrieval

An Ensemble Deep Learning and Modified Wild Horse Optimizati...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： S. Ramya V. Sumalatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Due to the restricted capacity for focus on the Human Visual System, clinical specialists may ignore minor lesions in different Medical Images (MI), making MI-based diagnosis a difficult process that could negatively impact clinical therapy. On the other hand, this issue could be handled through an effective Content-Based MI Retrieval (CBMIR) approach to examine comparable cases from the prior medical database. The retrieval engine utilizes Feature Vectors (FV), which are High-Level (HL) image representations maintained by a CBIR structure, to match and rank Query Images (QI) based on similarity. In this research work, an Ensemble Learning (EL) and Modified Wild Horse Optimization (MWHO) based pixel selection with Henon chaotic map (MWHO-HCE) is developed. Next, the image retrieval process includes a series of processes, namely Densenet-121-based feature extraction, Enhanced Sparrow Search Optimization Algorithm (ESSO) based Hyper Parameter (HP) optimizer, and Ensemble deep learning classifier (EDLC) such as Resnet50 inspectionV2 and Lenet are used to classify the lung diseases for the given dataset. Utilizing the elcap test image database, the suggested technique's effectiveness is evaluated. The study outcomes indicate the effectiveness of the suggested strategy for MIR (MI Retrieval).

关键词： Deep learning Computational modeling Pulmonary diseases Image retrieval Feature extraction Real-time systems Iron Optimization Medical diagnostic imaging Residual neural networks

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