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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： S Iniyan J ThangaKumar P. Selvi Rajendran Dept. of computer science and engineering Hindustan institute of science and technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

social media is useful in the community for sharing and discussing various events with others. The study's main objective is to predict the sign of the stock by sentimental analysis regarding the Hindenburg report on Adani group stocks by obtaining tweets from Twitter. The study's findings will aid in understanding the effect of the Hindenburg report on the stocks of the Adani group. Excel files are downloaded from Twitter containing tweets from social media users. Tweets are cleaned up by eliminating noise data after extraction. Machine learning algorithms, such as Logistic Regression (LR), Linear support classification (SVC), Random Forest (RF), and voting, were used to classify the data into positive, Neutral, and negative groups.

关键词： Radio frequency Logistic regression Sentiment analysis Machine learning algorithms Social networking (online) Blogs Noise

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Advanced Temporal Attention Mechanism Based 5G Traffic Prediction Model for IoT Ecosystems

Advanced Temporal Attention Mechanism Based 5G Traffic Predi...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Dharani Sabari Samudrala Sambit Kumar Mishra Rajiv Senapati Dept. of Computer Science & Engineering SRM University Ap India

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Traffic prediction in5G is important for effective deployment and operation of Internet of Things (IoT) ecosystems. It enables resource management and optimization, guaranteeing that the network can handle unpredictable traffic volumes with-out experiencing traffic jams. This helps to ensure high quality of service and low latency for applications such as autonomous automobiles and virtual reality. Predictive traffic management further enhances user experience by keeping services consistent and reliable, particularly during busy hours. There are various approaches to traffic prediction in 5G networks, and each has advantages and disadvantages of its own. The choice of model will depend on how precise, adaptable, and computationally demanding the network must be. The model proposed in this paper integrates lightweight convolution with temporal attention to deliver accurate and efficient traffic prediction for 5G networks that may further be useful for developing IoT ecosystem.

关键词： Solid modeling Adaptation models Attention mechanisms 5G mobile communication Convolution Computational modeling Ecosystems Virtual reality Predictive models Internet of Things

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Automated Framework for Segmenting and Identifying Thyroid Nodules in Ultrasound Images

Automated Framework for Segmenting and Identifying Thyroid N...

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International Conference on Electrical and computer engineering (ICECE)

作者： Md. Enamul Hoque Marzun Maria Sultana Boshita Roy Binti T. M. Shahriar Sazzad Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

Thyroid disease is among the most prevalent endocrine disorders, making early detection of thyroid nodules essential for effective management and treatment. The complex nature of thyroid nodules makes accurate detection challenging, even with advanced imaging techniques such as CT, MRI, and ultrasound. Traditional manual interpretation methods can be time-consuming and error-prone. This paper presents an automated framework that integrates advanced image processing techniques and machine learning approaches to enhance the detection and segmentation of thyroid nodules in ultrasound images. By employing the U-Net architecture alongside sophisticated image processing methods, the proposed system achieves a high accuracy rate of $99.1 \%$ on a large dataset. This framework not only accelerates and improves the accuracy of diagnoses but also reduces the workload for radiologists, representing a significant advancement in thyroid nodule detection.

关键词： Image segmentation Ultrasonic imaging Accuracy Design automation Magnetic resonance imaging Manuals Machine learning computer aided diagnosis Thyroid Diseases

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Detection of Malware Using Machine Learning techniques on Random Forest, Decision Tree, KNeighbour, AdaBoost, SGD, ExtraTrees and GaussianNB Classifier 4

Detection of Malware Using Machine Learning techniques on Ra...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Gupta, Rupesh Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

ISBN: (纸本)9798350305258

Spotting harmful software with machine learning models has become more crucial in cybersecurity. Machine learning can be used to recognize and sort harmful software by looking for specific patterns, behaviours, and features that set them apart from normal programs. It's important to understand that the world's access of harmful computer programs is always changing, and people who create these programs often try different methods to avoid getting caught. So, updating the model regularly and keeping an eye on it all the time are very important to making sure we can detect malware effectively. In the current digital world, malware is a big problem for cybersecurity. As bad software gets socially smarter and comes in many forms, old ways of finding and stopping it are not enough anymore. This is where models of machine learning start being useful. To summarize, a machine learning-based antivirus system can only be sustainable if it has good data, the right features, a good model, and skilled cybersecurity professionals working on it. This study focuses on Random Forest, Decision Tree, KNeighbour, AdaBoost and SGD classifiers' various classification methodologies for detection of malware. The proposed classifiers, which may be used for future study, emerge as the best classifiers for the dataset utilized for malware detection which help in global stability. © 2023 IEEE.

关键词： Anomaly Detection Artificial Intelligence Behaviour Analysis Classification Classifiers Data science Deep Learning Machine Learning Malware Attacks

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Students' Careers and AI: a decision-making support system for Academia

Students' Careers and AI: a decision-making support system f...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Bertini, Flavio Dal Palù, Alessandro Formisano, Andrea Pintus, Andrea Rainieri, Sara Salvarani, Luana Dept. of Mathematical Physical and Computer Sciences Parma University Italy Dept. of Mathematics Computer Science and Physics Udine University Italy Dept. of Humanities Social Sciences and Cultural Industries Parma University Italy Dept. of Engineering and Architecture Parma University Italy

In the peculiar realm of higher education, some of the challenges of Public Administration, in terms of quality assurance and data intelligence, can be addressed thanks to the complex ecosystem based on the careers of students and their engagement with the host academia. University governance, ranging from the university Rector and Quality Assurance committee to single heads of degree courses, needs to rely on quantitative and unbiased measures when designing and planning actions. This paper reports on an ongoing project started at Parma University in 2019, that has multiple goals: (1) to collect various sources of students' career-related raw data and to and provide simple access to aggregated analyses through a web portal;(2) to offer an AI based synthesis, in form of automatically generated reports in natural language;(3) to analyze data to detect and predict potential issues (e.g., students drop-out, classes attendance, graduation time estimations, blockages in the career) that can be promptly highlighted, for immediate intervention. As opposed to the majority of academic analytics implementations, particular care is devoted to minimizing ethics and privacy issues and adhering to explainable AI principles in the generation of synthetic explanations of charts and reports. The results of lines of research (2) and (3) will be integrated in the portal (1) that is currently deployed at Parma University. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Quality assurance

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Sumaiyya Khan Aashish Bardekar Dept. of Computer Science and Engineering Sipna College of Engineering & Technology Amravati India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content.

关键词： Training Measurement Translation Accuracy Optical character recognition Artificial neural networks Feature extraction Mathematical models Character recognition MATLAB

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Heart disease Classification Utilising Machine Learning Techniques

Heart disease Classification Utilising Machine Learning Tech...

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Advancement in Technology (ICONAT), International Conference for

作者： Shiv Ashish Dhondiyal Himanshu Verma Rohit Kumar Vikash Sawan Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Haridwar University Roorkee Department of Computer Engineering & Applications GLA University Mathura Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world’s biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994.

关键词： Heart Support vector machines Accuracy Machine learning algorithms Simulation Supervised learning Prediction algorithms Classification algorithms Diseases Testing

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Intensify Stroke Prediction Model Associated with Hypertensive State

Intensify Stroke Prediction Model Associated with Hypertensi...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Md. Rabiul Islam Mst. Sahela Rahman Radha Rani Paul Md. Fahad Hosen Mst. Khairunnahar Md. Julker Nayeem Dept. of Computer Science & Engineering Pundra University of Science & Technology Bogura Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Hypertension and stroke are significant worldwide health issues with high prevalence and complex health hazards. Hypertensive state implications include death or major complications such as myocardial infarction. This study leverages data from the Internet to foresee the prognosis for individuals with hypertensive strokes. There are two main components to the clinical outcome prediction in this study. The value of various variables is ranked by our model through the use of cross-validation techniques, recursive feature disposal, and classification algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our empirical cross-validation results demonstrate the remarkable predictive reliability of both LR and RF once repetitive features are removed, even with a smaller set of features and classifiers. Particularly in terms of accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), logistic regression performs better than the other two classifiers. Likewise, we conducted independent prediction analyses for hypertension, hypertensive stroke, and stroke. The highest result of 96.15% is obtained using logistic regression for stroke prediction. The random forest classifier has an accuracy of 91.25% for hypertension, and combining stroke with a hypertension support vector classifier gives 91.72% accuracy. This demonstrates how well it might provide patients with hypertensive strokes with precise and trustworthy projections. Finally, our study illuminates the potentially useful prediction powers of particular algorithms in determining the prognosis of patients with hypertensive strokes.

关键词： Hypertension Support vector machines Logistic regression Accuracy Stroke (medical condition) Vectors Classification algorithms

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