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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Jose, Andrews Juliet, Sujitha Anitha, J. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837242672

Liver tumors, including metastatic cancer and cholangiocarcinoma, pose a significant threat to global health, necessitating accurate and timely detection. This paper presents a novel deep learning model, SE-VGGNet, for liver tumor classification, which innovatively combines Squeeze-and-Excitation (SE) blocks with a VGGNet backbone. This integration enhances feature extraction by allowing the model to recalibrate channel-wise feature responses, focusing more effectively on the most relevant image regions. SE-VGGNet was evaluated on a liver tumor dataset, achieving a classification accuracy of 99.09%, precision of 98.15%, and recall of 100%, significantly outperforming traditional CNN and VGG-16 models. The novelty of this approach lies in its ability to achieve high performance even with small datasets, addressing a critical challenge in medical imaging. Additionally, this method offers the potential for broader application to other medical imaging tasks, highlighting its adaptability and impact on early cancer diagnosis and clinical decision-making. © The Institution of engineering & Technology 2024.

关键词： Deep learning

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

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computers, Materials & Continua 2023年第10期77卷 665-680页

作者： Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Ahmad Almadhor Tai-hoon Kim Department of Computer Science COMSATS UniversityIslamabadPakistan College of Computer Studies De La Salle UniversityManila1004Philippines Faculty of Information and Communication Studies University of the Philippines Open UniversityLos Banos4031Philippines College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia School of Electrical and Computer Engineering Yeosu CampusChonnam National UniversityYeosu-si59626Korea

Cardiac disease is a chronic condition that impairs the heart’s *** includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart *** conditions can lead to serious complications and even be life-threatening if not detected and managed in *** have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and *** approaches have been applied to predict and treat heart disease utilizing ML and *** paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease *** approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart *** models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart ***-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed *** reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

关键词： Deep neural network heart disease healthcare machine learning stacking

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RNNDRP: Developing Recurrent Neural Network Based Anticancer Drug Response Prediction Model 21

RNNDRP: Developing Recurrent Neural Network Based Anticancer...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Zaidi, Awais Raza Majid, Abdul Bilal, Muhammad Pakistan Institute of Engineering & Applied Sciences Department of Computer & Information Sciences Islamabad Pakistan

ISBN: (纸本)9798331516680

Due to the devastating nature of the cancer disease, patients have a high death rate in the worlde. For this purpose, pharmacologists, medical scientists, and biologists are making collaborating efforts to efficient medications for the treatment of cancer. In the past, all cancer types were treated with the same approach in a one-size-fits-all manner. In this research, we present Recurrent Neural Network-based Prediction Model (RNNDRP) that accurately predicts drug sensitivity on cell lines data of cancerous patients using GDSC dataset for training and testing. Our prediction model accurately predicts minimal inhibitory concentration (IC50) for various drugs. Our RNNDRP model has obtained the RMSE and R2 scores of 0.814 and 0.875, respectively. The performance has shown to be better than existing state of the art models developed for drug sensitivity. These improved results validated the model's potency due to hybrid features in addressing the problem of anticancer drug response prediction. The proposed model results might reduce the cost and time elapsed on lab tests for cancer treatment. © 2024 IEEE.

关键词： Prediction models

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Lexicon-based fine-tuning of multilingual language models for low-resource language sentiment analysis

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1116-1125页

作者： Vinura Dhananjaya Surangika Ranathunga Sanath Jayasena Department of Computer Science and Engineering University of MoratuwaMoratuwaSri Lanka School of Mathematical and Computational Sciences Massey UniversityPalmerston NorthNew Zealand

Pre-trained multilingual language models (PMLMs) such as mBERT and XLM-R have shown good cross-lingual transferability. However, they are not specifically trained to capture cross-lingual signals concerning sentiment words. This poses a disadvantage for low-resource languages (LRLs) that are under-represented in these models. To better fine-tune these models for sentiment classification in LRLs, a novel intermediate task fine-tuning (ITFT) technique based on a sentiment lexicon of a high-resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3-class sentiment classification task and show that this method outperforms vanilla fine-tuning of the PMLM. It also outperforms or is on-par with basic ITFT that relies on an HRL sentiment classification dataset.

关键词： deep learning natural languages natural language processing

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Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

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Scalable SubXPCA on a Distributed Platform Applied to Classification of Hyperspectral Images 8

Scalable SubXPCA on a Distributed Platform Applied to Classi...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Rupa, Bogolu Negi, Atul School of Computer and Information Sciences University of Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331531539

The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributed processing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributed processing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributed processing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version. © 2024 IEEE.

关键词： Dimensionality reduction

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal 2

Optimizing Waste Management: Integrated Pollution Detection ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Samuel Prabakaran, M. Sophia, S. Sophas Samuel, S. Karunya Institute of Technology and Sciences Department of Computer Engineering Coimbatore India Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems. © 2024 IEEE.

关键词： Data acquisition

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New Identities for Moore-Penrose Inverses of Some Operator Products and Their Reverse-order Laws

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Acta Mathematica Sinica,English Series 2023年第12期39卷 2414-2426页

作者： Mehdi MOHAMMADZADEH KARIZAKI Javad FAROKHI-OSTAD Department of Computer Engineering University of Torbat HeydariehTorbat Heydarieh***Iran Department of Basic Sciences Birjand University of TechnologyBirjand***Iran

We establish new identities for Moore-Penrose inverses of some operator products,and prove their associated reverse-order ***,our results concerning the Moore-Penrose inverse of a product of two operators lead in find... 详细信息

关键词： Moore-Penrose Inverse Reverse-order Law

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