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Predictive Analysis for User Mobility Using Geospatial data 3rd

Predictive Analysis for User Mobility Using Geospatial Data

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3rd international conference on Recent Innovations in Computing, ICRIC 2020

作者： Verma, Jai Prakash Tanwar, Sudeep Desai, Archies Khatri, Poojan Polkowski, Zdzislaw Institute of Technology Nirma University Ahmedabad Gujarat India Wroclaw University of Economics and Business Wroclaw Poland

ISBN: (纸本)9789811582967

Extremely usage of smart wearable devices such as smartphones and smartwatches which contain various sensors for location detection such as Wi-Fi, LTE, GPS and motion detection such as accelerometer, it has become easier to obtain user mobility data. Today communication systems are becoming more popular due to the developments in communication technologies. There are various services provided which also help to access the data such as video, audio, images from which we can be used to grab the information or pattern of user mobility. The user mobility where user’s movements and locations can be predicted using various methods and algorithms. It can be predicted through data mining, machine learning, and deep learning algorithms where user’s data are fetched from the communication system. A comparative data mining model base on DBSCAN and RNN-LSTM was proposed for predicting the user’s future location-based information predicted from the last locations reported. Mobility prediction based on the transition matrix prediction is done from cell to cell and calculated with the help of the previous inter-cell movement. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Location

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Fake news detection using machine learning methods 3

Fake news detection using machine learning methods

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3rd international conference on data Science, E-learning and Information Systems, data 2021

作者： Nagaraja, Arun Soumya, K.N. Naik, Prajwal Sinha, Anubhav Rajendrakumar, Jain Vinay ISE Department SET- Jain Deemed to be University Karnataka Bengaluru India

ISBN: (纸本)9781450388382

The paper is about the detection of unauthenticated news using machine-learning methods with different algorithms. There is lot of scope to check the reality of the news received from various sources like websites, blogs, e-content. To identify the fake news, there is a need of some application in real time. Many methods were proposed earlier to observe fake news such as style-based, propagation-based and user-based. Automatic fake news detection application can be generated using natural language processing, information retrieval techniques, as well as graph theory. Language modeling is used to predict the missing or next word in a sentence based on the context. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers;most likely, it is done to increase the number of visitors on that particular page so that with an increasing number of visitors the page could claim more advertisement. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect the news is FAKE or REAL. Various methods are used for collecting the data and the data mining techniques are applied to clean and visualize it. data mining helps to differentiate between the qualities of data depending upon its properties. The performance of detecting news only from the body of news is not sufficient but also social engagements should be considered. The objective of the work is to provide end-users with a robust solution so that they can figure out phishy and misguiding information. This technique combines the title and the body of the news to predict fake news more efficiently. The application is concerned with finding a result that could be used to identify fake news to help users. © 2021 Association for Computing machinery.

关键词： machine learning

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Language based object detection with human body using CLIPseg : Integrating CLIP and Optical Flow for Unsupervised Object Tracking and Keypoint Detection 3

Language based object detection with human body using CLIPse...

引用

3rd international conference on Image Processing, Computer Vision and machine learning, ICICML 2024

作者： Wen, Bo New York University Department of Computer Science New York United States

ISBN: (纸本)9798350355413

Human pose estimation and keypoint detection are essential tasks in computer vision, with widespread applications in areas such as action recognition, human-computer interaction, sports analytics, and medical imaging. Recent advances in deep learning and optical flow models have revolutionized these fields, enhancing the precision and scalability of tracking human motion. In areas that're not human related or in the field of object tracking in general Challenges arise in settings calling for adaptable frameworks. The main focus of this research is, on understanding how to accomplish recognition. Monitoring of moving points, on various objects without the need, for manual intervention. We demonstrate how combining CLIP and CLIPSeg models, with optical flow enhances annotations. With methods, like Co tracker and Dense Optical Flow, at our disposal we can carry out tasks without supervision. keypoint identification and continuous tracking, across frames, in a video sequence are highlighted in our findings showcasing. This method effectively removes the necessity, for annotations while still upholding standards. Significant progress has been made in identifying and monitoring points accurately marking an advancement, over previous methods. current approaches that heavily depend on existing labeled data. This represents a significant improvement over existing methods that heavily rely on pre-labeled data. The integration of optical flow with CLIP-based models allows for real-time tracking and motion prediction in complex, dynamic scenes, extending the applicability of keypoint detection beyond human pose estimation to arbitrary objects. This approach has broad implications for real-world applications such as video surveillance, autonomous navigation, and sports performance analysis, where unsupervised tracking can provide greater adaptability and efficiency. By eliminating manual intervention, our method sets a new standard for scalability and accuracy in dynamic trackin

关键词： Unsupervised learning

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Different Techniques for Detecting Plant Leaf Disease Using machine learning 1

引用

3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Gupta, Ashish Gupta, Sanjeev Kumar Yadav, Pritaj Gupta, Deepak Department of CSE Rabindranath Tagore University Bhopal India Department of CSE Institute of Technology and Management Gwalior India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Agriculture is India’s backbone, and it helps to expand the country’s economic system. Plant disease identification is an important aspect of agriculture. Plant diseases have an impact on quality, quantity, and productivity. The most important exercise for increasing production is plant disease predictions. In this research study, the author presents a comparative examination of many types of plant leaf disease as well as all of the research approaches related to plant leaf disease. This was done in the past for two reasons: To keep the paper’s size down, and because there are a few quirks in the procedures for dealing with seeds, roots, and herbal items that would demand a separate investigation. As stated by their purpose, the selected proposition is divided into three instructions: recognition, seriousness measurement, and characterization. As a result, each of these groups is segmented using the primary specialized association used in the calculation. By giving a complete and open definition of this broad topic, this study hopes to assist experts in both vegetable diseases and case recognition. The object of work will be a valuable resource for researchers looking to identify specific types of plant diseases using data-driven methodologies. The development of mobile-based applications based on the investigated ML/DL techniques will undoubtedly improve crop production. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Agriculture Disease detection classification features extraction Image acquisition Pre-processing

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SCHEMATA: 3D Classification and Categorization of Ancient Terracotta Figurines 12

SCHEMATA: 3D Classification and Categorization of Ancient Te...

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12th international conference on Agents and Artificial Intelligence (ICAART)

作者： Zeckey, Alexander Langner, Martin Georg August Univ Gottingen Inst Digital Humanities Heinrich Duker Weg 12 Gottingen Germany

ISBN: (纸本)9789897583957

The goal of the starting case-study is not only to develop procedures for automatically generating corpora using 3D pattern recognition, but also to reflect on the associated schematizations and how they can be applied in computer science and visual sciences. For this purpose, methods of object mining in 3D data are to be developed. We chose an object group which is defined by its complexity in shape and the similarity between the objects: In 4th and 3rd century BC ancient Greece small terracotta figurines used to be an art form that was quite common. Based on 200 of those terracottas, a classification system will be elaborated with digital methods, which is able to meet the complexity of the artefacts. In close cooperation between computer science and archaeology, this experimental process leads to a fundamental examination of the concept of pattern recognition as a humanities category. The discussion of the various concepts and methods will be carried out in two complementary dissertations.

关键词： Object mining Image Exploration Greek Terracotta Figurines machine learning Typology

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Detection of Error-related Brain Oscillatory Activity using machine learning 3

Detection of Error-related Brain Oscillatory Activity using ...

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3rd international conference "Neurotechnologies and Neurointerfaces", CNN 2021

作者： Ivanitsky, Georgy Malinovskaya, Polina Balaev, Vladislav Martynova, Olga Laboratory of Human Higher Nervous Activity Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Science Moscow Russia

ISBN: (纸本)9781665424745

Resting-state EEG (rsEEG) can reflect error-related activity formed during and after solving a cognitive task. We analyzed EEG data of 19 healthy volunteers who solved mental tasks (verbal – anagram, and spatial – assembling a figure) presented on a computer screen. The maximum allowed solving time was 40 s, but if a subject was ready with an answer sooner than this period, he/she pressed a button causing a task to disappear. In between tasks, participants observed a black screen for about 6 s. Applying two machine-learning algorithms (Perceptron without hidden layers, and k nearest neighbors) with spectral patterns of rsEEG as input vectors, we tested the accuracy of discriminating inter-stimulus rsEEG after correctly solved tasks from rsEEG after erroneously solved tasks, as well as rsEEG after completed tasks from rsEEG after uncompleted tasks. K nearest neighbors (KNN) classifier performed better than Perceptron in recognition of rsEEG after correct versus incorrect answers (75% of accuracy rate). Both algorithms showed robust accuracy rates (more than 71%) in the detection of rsEEG after completed versus uncompleted tasks. Our findings indicate that both erroneous answers and the incompleteness of the task are reflected in the rsEEG rhythmic patterns. The obtained results suggest that the neural activity elicited by goal-directed learning and error-monitoring is not only response-locked but may retain traces for several seconds after the response. © 2021 IEEE.

关键词： Errors

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Financial Credit Risk Measurement Using a Binary Classification Model 3rd

Financial Credit Risk Measurement Using a Binary Classificat...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

作者： Chiluiza, Oscar Guevara-Vega, Cathy Quiña-Mera, Antonio Landeta-López, Pablo Montaluisa, Javier Universidad de las Fuerzas Armadas ESPE Quijano y Ordoñez Street Latacunga Ecuador eCIER Research Group Universidad Técnica del Norte 17 de Julio Avenue Ibarra Ecuador

ISBN: (纸本)9783031322129

Currently, financial institutions have several problems in the analysis of information to grant a credit or a loan, causing losses that involve collection expenses, notifications, legal processes, among others. Thanks to the digital transformation and technological progress, Artificial Intelligence and especially machine learning can be used to analyze customer data and predict non-compliance with the payment of their obligations to institutions. The objective of this work is to apply CRISP-DM (Cross Industry Standard Process for data mining) methodology and the Random Forest (XGBoost), Logistic Regression and Neural Networks of supervised learning models, to implement a predictive model that allows evaluating credit risk. With the result of the application of the predictive model, it is concluded that the use of machine learning tools helps to optimize the evaluation of credit risk in financial entities. Once CRISP-DM methodology was used for the analysis, development, and evaluation of the models, it was concluded that the most efficient model is the Random Forest. Based on this experience, future work could implement this type of model in other areas such as: fraud detection, customer segmentation or a recommendation engine that can suggest financial products and services based on customer needs and behaviors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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data preprocessing for learning, analyzing and detecting scene text video based on rotational gradient 3

Data preprocessing for learning, analyzing and detecting sce...

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3rd international conference on data Science, E-learning and Information Systems, data 2021

作者： Devi, Mortha Manasa Seetha, Maddala Raju, S. Viswanadha Dept of CSE JNTU Hyderabad India KBC Bank en Verzekeringen Brussels Belgium Department of CSE G.Narayanamma Institute of Technology and Science Hyderabad India Department of CSE Jawaharlal Nehru Technological University Hyderabad College of Engineering Jagtial Hyderabad India

ISBN: (纸本)9781450388382

Challenging annotated video datasets are in huge demand for the researchers and embedded industrials to learn and build an artificial intelligence for detecting, localizing and classifying the objects of interest aimed at various applications under pattern recognition and computer vision domain. It is very significant to produce those annotated sets to the respective communal. This paper focuses on text as annotated data in video for detection, localization, tracking and classification to solve several optical character recognition (OCR) based problems. Text is very essential in understanding the nature of the video because of diverse applications which are in renowned today like video retrieval and searching, driverless cars, industrial goods automation, geocoding and many more. Hence, it is important to understand how to create, prepare and load datasets to make ready for the machine to learn and understand. First, we have applied bilateral filter to preserve the edge information. Then, rotational gradient approach is proposed to detect the text in variable viewpoints. Later, the combination of morphology and contours has applied to generate blobs with bounding box around the detected regions by eradicating quasi text areas. The simulation results have shown better performance than traditional techniques with better detection rate on ICDAR Robust Reading Competition on Text in Video 2013-15 datasets. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Morphology

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On data-Driven Approaches for Presentation Attack Detection in Iris recognition Systems 3rd

On Data-Driven Approaches for Presentation Attack Detection ...

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3rd international conference on Recent Innovations in Computing, ICRIC 2020

作者： Sharma, Deepika Selwal, Arvind Department of Computer Science & Information Technology Central University of Jammu J 181143 India

ISBN: (纸本)9789811582967

With the development of modern machine learning-based techniques for accurate and efficient classification, the paradigm has shifted to automatic intelligent-based methods. The iris recognition systems constitute one of the most reliable human authentication infrastructures in contemporary computing applications. However, the vulnerability of these systems is a major challenge due to a variety of presentation attacks which degrades their reliability when adopted in real-life applications. Hence, to combat the iris presentation attacks, an additional process called as presentation attack detection mechanism is integrated within the iris recognition systems. In this paper, a review of the modern intelligent approaches for iris presentation attack detection (PAD) mechanisms is presented with a special focus on the data-driven approaches. The presented study shows that the machine learning-based approaches provides better classification accuracy as compared to conventional iris PAD techniques. However, one of the open research challenge is to design the robust intelligent iris PAD frameworks with cross-sensor and cross-database testing capabilities. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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recognition of Speech Emotion Using machine learning Techniques 3rd

Recognition of Speech Emotion Using Machine Learning Techniq...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Chaurasiya, Akash Garg, Govind Gaud, Rahul Chakraborty, Bodhi Gupta, Shashi Kant ITM University Gwalior MP Gwalior India

ISBN: (纸本)9783031431449

Speech emotion recognition (SER) is a crucial portion of interaction between computers and machines. In this venture, we aim to classify speech that is taken as fair, joyful, sorrowful, amazed, panic, dislike, fume. Speech emotion recognition frameworks recognize feelings from the human voice in the areas of hospitality, the automobile industry, telecom centers, programmed interpretation frameworks, and human-machine interaction like robots. The Audio dataset was created by combining sound recordings from the Ryerson Audiovisual database of Emotional Speech and Song (RAVDESS) and the Toronto Speech Set data (TEES).The relevance of these highlighted feeling classifications is contrasted using methodologies such as Long Short Term Memory (LSTM), Decision Tree, MLP Classifier and K-Nearest Neighbour (KNN). In this classification 7 feelings are present. The LSTM training model achieved 92.5% accuracy;the Decision Tree training model achieved 100% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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