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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： C. Malarvizhi A. Muruganantham Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Science (PG) Kristu Jayanti College Bengaluru India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

Intervertebral discs, the rubbery cushions between spinal bones, can bulge or rupture (herniate) beyond their normal boundaries. This can irritate nearby nerves, causing pain, particularly in the upper back with cervical disc herniation. This work explores the use of morphological transformations of image and simulated neural network classification to analyze magnetic resonance imaging (MRI) data for automatic detection of disc herniation. Morphological operations manipulate individual pixels in an image based on the measurement of their surrounding pixels. In this paper, these techniques are employed for herniated regions identified in the magnetic resonance image scans. Specifically, the edge-detected canny image is suitable for identifying the edges of these regions, followed by morphological operations with structuring elements to refine and enhance the image quality. Using non-flat structuring elements such as arbitrary and ball applied to the images. Morphological operations named dilation and erosion are applied for four stages of herniated canny images. A neural network classification method is employed to analyze the processed images at different stages of disc herniation. This allows for the calculation of the effectiveness of each structuring element in accurately identifying herniated discs. The results of the morphological operations could be used to extract quantitative measurements of hernia size, shape, or other relevant features, aiding in treatment planning and monitoring.

关键词： Shape Magnetic resonance imaging Image edge detection Shape measurement Neural networks Feature extraction Size measurement

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Cloud-Driven Wearables using Naive Bayes Algorithm and advanced Sensors in Smart Fabrics 3

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Praveena, M.D. Anto Gupta, Nukala Sujata Marin, V. Bini Jeslin, J Gnana Meenakshi, R. Muthulekshmi, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world. © 2024 IEEE.

关键词： Smart textiles

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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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Identifying the best common factor model via exploratory eactor analysis

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 24-33页

作者： HE Bao-hua TANG Rui TAGN Qi-yi School of Big Data Fuzhou University of International Studies and TradeFuzhou 350202China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Department of Computer Science University of BathUK Institute of Insect Science Zhejiang UniversityHangzhou 310058China.

Currently,there is no solid criterion for judging the quality of the estimators in factor *** paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor *** proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor *** are presented to demonstrate how the method is implemented and to verify its effectiveness.

关键词： factor analysis Shapiro-Wilk normality residuals

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Next Word Prediction Using Deep Learning

Next Word Prediction Using Deep Learning

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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Tiwari, Aditya Sengar, Neha Yadav, Vrinda Centre for Advanced Studies Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

Next Word Prediction involves guessing the next word which is most likely to come after the current word. The system suggests a few words. A user can choose a word according to their choice from a list of suggested word by system. It increases typing speed and reduces keystrokes of the user. It is also useful for disabled people to enter text slowly and for those who are not good with spellings. Previous studies focused on prediction of the next word in different languages. Some of them are Bangla, Assamese, Ukraine, Kurdish, English, and Hindi. According to Census 2011, 43.63% of the Indian population uses Hindi, the national language of India. In this work, deep learning techniques are proposed to predict the next word in Hindi language. The paper uses Long Short Term Memory and Bidirectional Long Short Term Memory as the base neural network architecture. The model proposed in this work outperformed the existing approaches and achieved the best accuracy among neural network based approaches on IITB English-Hindi parallel corpus. © 2022 IEEE.

关键词： Long short-term memory

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Cataract Detection using Deep Learning Model on Digital Camera Images 6

Cataract Detection using Deep Learning Model on Digital Came...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Chaudhary, Raghavendra Kumar, Arun Centre for Advanced Studies Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

Cataracts are one of the most prevalent visual diseases that people get as they gets older. A cataract is a fog that forms on the lenses of our eyes. The main symptoms of this illness include dim view, colorless, and difficulties in watching a daylight. Slit lamps and fundus cameras are routinely used to detect cataracts, although they are both expensive and require domain knowledge. As a result, the shortage of skilled ophthalmologists may cause cataract identification to be delayed, necessitating medical treatment. Consequently, early detection and prohibition of cataracts might assist to reduce the frequency of occurrence of blindness. Hence the goal of this study is to utilize Convolutional Neural Networks (CNN) to diagnose cataract pathology using a publicly available Digital Camera Image dataset. The CNN cycle takes a considerable amount of time and expense. As a result, optimization will take place. It can increase accuracy while also reducing processing time. In this study the proposed model consist of three Convolutional layers, three pooling layers, one flatten layer, and two dense layers with an ADAM optimizer. The proposed CNN model can detect cataracts with a testing accuracy of 0.9925 with a loss of 0.0475, and a training accuracy of 0.9980 with loss of 0.0038, for the selected Digital Camera Images Dataset. © 2022 IEEE.

关键词： Convolutional neural networks

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Machine Learning based Prediction of Parkinson's Diseases

Machine Learning based Prediction of Parkinson's Diseases

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Arockiya Selvi S T. Kamalakannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Parkinson's disease(PDs) is a neurological disorder characterized by accidental or uncontrollable movements such as shaking, rigidity, and trouble with balance and coordination. Symptoms are usually minor at first and intensify over time. PD datasets tend to be compact and inadequately diversified. Several datasets contain incorrect values or discrepancies as a result of diagnostic procedure inconsistencies. Datasets frequently have fewer PD patients than healthy individuals, resulting in biased models. SVM is a common method for PD prediction due to its ability to handle tiny, high-dimensional data sets and its robust classification skills. SVM performs effectively even with small medical datasets, unlike deep learning models, which require enormous volumes of data. SVM minimizes overfitting, particularly when working with tiny datasets, making it more dependable for use in real-world medical settings. The study employed SVM algorithms to predict Parkinson's disease. Additionally, disease data is classified and compared using SVM with different kernel parameters. The study compares the performance of four alternative SVM models Linear SVM (LSVM), Quadratic SVM (QSVM), Cubic Gaussian SVM (CGSVM), and Medium Gaussian SVM (MGSVM) using five assessment metrics: accuracy, sensitivity, specificity, precision, and F1-Score. MGSVM is the most effective model for predicting PDs, with the highest precision and consistency.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Sensitivity Parkinson's disease Predictive models Prediction algorithms Classification algorithms Medical diagnostic imaging

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： V. Ramya K.S. Thirunavakkarasu Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively.

关键词： COVID-19 Measurement Deep learning Accuracy Pandemics Computational modeling Predictive models Prediction algorithms Data models Random forests

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SCSYGPS: Students Course-Wise, Semester-Wise and Year-Wise Grade Prediction System in Education Sector Using Supervised Learning 2

SCSYGPS: Students Course-Wise, Semester-Wise and Year-Wise G...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Dol, Sunita M. Jawandhiya, P.M. Pankaj Laddhad Institute of Technology and Management Studies Computer Science and Engineering Department Maharashtra Buldana443002 India Pankaj Laddhad Institute of Technology and Management Studies Maharashtra Buldana443002 India

ISBN: (纸本)9798350328202

Educational Data Mining (EDM) is the research field that deals with the application of Data Mining techniques on the data obtained from education sector. Predicting students' academic performance is of paramount importance to students, instructor, and educational institutes. In this research article, a framework for prediction of students' performance in particular course, semester and year is developed and named it as SCSYGPS (Students' Course-wise, Semester-wise, and Year-wise Grade Prediction System). In this framework, data is collected from university result ledgers related to four year computer science and Engineering Programme and prepared nine datasets. Eight classification algorithms - Random Forests (RF), Linear Support Vector Classifier (LSVC), Gradient Boosting (GB), K-Nearest Neighbour (KNN), AdaBoost, XGBoost, LightGBM, and Linear Discriminannt Analysis (LDA) are applied one by one on nine datasets. The classification algorithm that works best for each dataset is selected based on performance parameters values such as Accuracy, F1-Score, Precision, Recall, and Mean Absolute Error (MAE). The classification algorithm which has the highest value of performance parameters accuracy, f1-score, precision, recall, and lowest value of MAE is considered as the best classification algorithm for that dataset. For seven datasets, LightGBM is found to be the best classification algorithm while XGBoost and AdaBoost is noted to be the best classification algorithms for two datasets with accuracy 97% nd 95% respectively. © 2024 IEEE.

关键词： Classification (of information)

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