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TweetSense: Navigating Disaster Signals Using Machine Learning and Deep Learning Techniques

TweetSense: Navigating Disaster Signals Using Machine Learni...

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international conference on computing and Networking Technology (ICCNT)

作者： R. Shanthakumari E.M. Roopa Devi S. Vinothkumar N.Asifaa Sulthana B.Fahima Begum G. Kaushik Information Technology Kongu Engineering College

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A disaster is a sudden, catastrophic event that causes significant damage, destruction, and disruption to the normal functioning of society, often resulting in loss of life, property, and livelihoods. Disasters can be natural (earthquakes, floods, wildfires) or human-made (industrial accidents, terrorist attacks, wars). Disasters can have wide-ranging impacts on communities, economies, and the environment, requiring emergency response and recovery efforts to mitigate their effects and restore normalcy. With disasters being too frequent in the modern world, the analysis of tweets cannot be an easy task for manual analysis. To overcome this, an automated model is to be built, which can reduce the manual analysis time for emergency responders. Socialmedia, with Twitter being a prime example, acts as a primary channel for disseminating information regarding disasters. The ability to predict relevant tweets holds immense potential in the development of early warning systems, facilitating timely actions by authorities and emergency responders to mitigate the impact of disasters. The approaches followed to achieve the tweet classification are Bag of Words, Term Frequency-Inverse Document Frequency, Word2Vec, followed by machine learning & deep learning methodologies. The CNN model is notably achieving a higher accuracy of $\mathbf{8 4 . 1 \%}$ compared to other models.

关键词： Deep learning Wildfires Social networking (online) Disasters Terrorism Blogs Text categorization Manuals User interfaces pattern recognition

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STClass: A Method for Determining the Sensitivity of Documents 20th

STClass: A Method for Determining the Sensitivity of Documen...

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20th Mexican international conference on Artificial Intelligence (MICAI)

作者： Morales Escobar, Saturnino Job Ruiz Shulcloper, Jose Juarez Landin, Cristina Ruiz-Castilla, Jose-Sergio Perez Garcia, Osvaldo Andres Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Valle Mexico Mexico City DF Mexico Univ Informat Sci Res Grp Log Combinatorial Pattern Recognit Invest Havana Cuba Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Valle Chalco Valle De Chalco Valle De Chalco Mexico Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Texcoco Texcoco Estado De Mexic Mexico CENATAV DATYS Equipo Invest Min Datos Havana Cuba

ISBN: (纸本)9783030898205;9783030898199

The leakage of sensitive information is a pressing problem when information is processed digitally due to the economic, political and social repercussions that it can cause to its owner. Despite the risks and possible threats, the information must always be kept available to users, therefore, alternatives must be available to protect, detect, and prevent the leakage of sensitive information. A particular case of this problem is the leakage of sensitive textual documents. However, the identification of unstructured sensitive information is a problem whose solution is not totally satisfactory despite the development of methods and applications with promising results. Thus, it is necessary to continue developing methods that contribute to the effective solution of the problem based on a critical analysis of existing techniques and their future projections. In this work we start from a taxonomy of the approaches with which this problem has been approached. From the taxonomy, the critical analysis of the techniques and above all considering the practical needs, a method of solution to the problem of determining the sensitivity of textual documents is proposed from the perspective of Logical Combinatorial patterns recognition. The problem is approached as a supervised classification problem with two classes: sensitive and non-sensitive textual documents. The proposal in this work is the STClass method to determine the sensitivity of documents, which consists of two phases: the training phase, where the parameters for classification are defined and the classification phase. With the datasets used, 96% of the well classified documents were reached.

关键词： Data leakage Sensitivity of documents Supervised classification Logical combinatorial pattern recognition

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Musical Brainwaves: Decoding Electroencephalography Signals Elicited by Musical Stimuli

Musical Brainwaves: Decoding Electroencephalography Signals ...

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computing, Sciences and Communications (ICCSC), international conference on

作者： Ankitha C Hita Juneja Ishita Bharadwaj Jeffrey S Varghese Prasad B. Honnavalli Gowri Srinivasa Dept. of Computer Science Engg. PES University Bengaluru India Dept. of Computer Science Engg. PES Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India Dept. of Computer Science Engg. PES Center of Pattern Recognition PES University Bengaluru India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

The influence of music on the human brain has been recognized for centuries, but recent advancements in brain-computer interfaces (BCIs) offer unparalleled opportunities to probe its complexities. This study investigates the effectiveness of music as a stimulus for driving BCIs using electroencephalography (EEG) signals. We analyze the neural correlates of music perception and imagination in the OpenMIIR dataset, employing advanced machine learning algorithms for EEG decoding. Exploiting various feature engineering techniques in time domain and frequency domain, we leverage convolutional neural networks and speech-inspired architectures like VLAAI to reconstruct the original audio stimuli from EEG data. Our results demonstrate a significant improvement in reconstructing music stimuli from EEG signals in the time domain, achieving a Pearson correlation of 0.35 compared to the current state-of-the-art performance of 0.11. Similarly, an increase in accuracy in classifying lyrical and non-lyrical musical excerpts based on participants' EEG responses was observed, rising from 49.6% to 57%.

关键词： Frequency-domain analysis Data preprocessing Music Feature extraction Electroencephalography Multiple signal classification Decoding Convolutional neural networks Time-domain analysis Image reconstruction

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Harnessing Residual Attention Networks for Stress Level Classification Using EEG Spectrograms

Harnessing Residual Attention Networks for Stress Level Clas...

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international conference on Informatics and computing (ICIC)

作者： Sza Sza Amulya Larasati Fitra Abdurrachman Bachtiar Intelligent System Laboratory Computer Science Faculty Universitas Brawijaya Malang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Stress detection is becoming one of the important applications in neuroscience and mental health. Conventional measurement of stress levels is often done by self-reported methods such as through filling out questionnaires, which rely heavily on individual perceptions and tend to be subjective because they are influenced by personal biases. To overcome this problem, a more objective approach is proposed by analyzing Electroencephalogram (EEG) signals that capture brain activity. Increasing detection accuracy is now supported by many developments in machine learning. This study focuses on stress level classification using spectrogram images from EEG signals. The main objective of this study is to develop a detection model that can identify stress levels based on EEG data. In this study, EEG signals were processed through several steps, including segmentation, filtering, and conversion into spectrograms. Furthermore, the Residual Attention Network model was tested to classify stress levels. The results of this study indicate that the spectrogram has excellent potential in recognizing stress patterns. This study produced a training accuracy of 0.77 with a testing accuracy of 0.53. These findings suggest that integrating more sophisticated architecture and preprocessing methods could significantly enhance the accuracy and reliability of stress detection models. This improvement has the potential to lead to more effective and timely interventions in mental health care in the future.

关键词： Training Accuracy Mental health Brain modeling Electroencephalography pattern recognition Reliability Spectrogram Testing Stress measurement

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An improved machine learning model for data analysis in Big-data environment

An improved machine learning model for data analysis in Big-...

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international conference on computing and Networking Technology (ICCNT)

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta T. Kiruthiga Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments &Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract offers an outline of the ability of artificial Intelligence (AI) and device studying (ML) answers for managing leverage threats in banking. Leverage is a key element in banking danger management, as it could expose the organization to disproportionate liabilities if left unchecked. AI and ML may be used to automate the tracking system of leverage hazards, allowing extra correct and welltimed identification and management of exposures. The abstract examines how AI/ML-driven solutions can leverage to be had facts to detect subtle modifications in leverage through the years, combining techniques from supervised and unsupervised ML to generate actionable insights. It then opinions viable implementation fashions and capacity challenges and gives first-rate practices for a hit implementation. The summary concludes that AI/ML-driven hazard management structures offer a promising new approach to leverage threat management inside the banking sector. In addition, research is warranted on this location.

关键词： Analytical models Data analysis Banking Organizations Machine learning Hazards Data models pattern recognition Artificial intelligence

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Fast pattern Spectra Using Tree Representation of the Image for Patch Retrieval 1

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Mirmahboub, Behzad More, Jerome Youssefi, David Giros, Alain Merciol, Francois Lefevre, Sebastien IRISA Univ Bretagne Sud UMR 6074 F-56000 Vannes France CNES Ctr Natl Etud Spatiales Toulouse France

ISBN: (数字)9783030766573

ISBN: (纸本)9783030766566;9783030766573

We extend the notion of content based image retrieval to patch retrieval where the goal is to find the similar patches to a query patch in a large image. Naive searching for similar patches by sequentially computing and comparing descriptors of sliding windows takes a lot of time in a large image. We propose a novel method to compute descriptors for all sliding windows independent from number of patches. We rely on tree representation of the image and exploit the histogram nature of pattern spectra to compute all the required descriptors in parallel. Computation time of the proposed method depends only on the number of tree nodes and is free from query selection. Experimental results show the effectiveness of the proposed method to reduce the computation time and its potential for object detection in large images.

关键词： Content based image retrieval Patch retrieval Tree representation pattern spectra Large satellite images

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Extracting User Daily Routine Activity patterns from UWB Sensor Data in Indoor Environment 29th

Extracting User Daily Routine Activity Patterns from UWB Sen...

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29th international conference on Collaboration Technologies and Social computing (CollabTech)

作者： Fawad, Muhammed Rahim Hayama, Tessai Nagaoka Univ Technol Niigata 16031 Japan

ISBN: (纸本)9783031421402;9783031421419

In recent years, location-based technologies for ubiquitous environments have transformed individuals' current location data into valuable assets. To establish advanced indoor location-based services, highly accurate positioning technology is required to precisely recognize and predict the movements of humans and objects. Hence, we proposed a method for extracting a user's activity patterns from time-series ultra-wideband (UWB) tag data in indoor environments. The proposed method consists of three steps: 1) estimate the user's stay regions from the user's location history using UWB sensors attached to the user, 2) assign each stay region to significant indoor activities, and 3) mine the activity patterns and their characteristics of the user from the sequence of indoor activities. In our experiments, we confirmed that the proposed method performed better in recognizing activity regions indoors and that the activity patterns of each member in the laboratory were discovered in a practical environment using the proposed method.

关键词： Human Activity recognition Indoor Location-Based System Human Activity pattern Analysis Sensor Network

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Face recognition system using a hybrid scale invariant feature transform based on local binary pattern

Face recognition system using a hybrid scale invariant featu...

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international conference on Image Processing and Capsule Networks, ICIPCN 2020

作者： Koteswara Rao, M. Veera Swamy, K. Anitha Sheela, K. ECE Department JNTUCEH HyderabadTelangana India ECE Department Vasavi College of Engineering HyderabadTelangana India ECE Department College of Engineering Hyderabad JNTUH HyderabadTelangana India

ISBN: (纸本)9783030518585

In this paper, the face recognition system using a hybrid scale-invariant feature transform (SIFT) based on local binary pattern (LBP) has been implemented. The face recognition system is preprocessed to extract the features based on SIFT and LBP. The modified local binary pattern is extracted the local features on the binary pattern on each subblock of the image. Histogram is implemented on each subblock of the binary number of the neighbor values. Modified scale-invariant feature transform is preprocessed to extract the features based on the scaling factors and various orientations of the images. This proposed method is classified by the support vector machine. The experimental results have been executed on the ORL, YALE databases, and based on the performance measures in terms of precision, recall, and F-Score. This paper gives better as compared to existing models. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Local binary pattern

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Parkinson's Diagnosis Through the Use of AI Throught Speech and Gait

Parkinson's Diagnosis Through the Use of AI Throught Speech ...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Asha V Seema Garg Manish Gupta Saurabh Sharma K. Jyothsna Reddy Mohammed Wael Mohammed Department of Computer Applications New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias University Gerater Noida Uttar Pradesh Lovely Professional University Phagwara India Lloyd Law College Greater Noida Uttar Pradesh India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Parkinson's disease is a chronic movement disorder that primarily affects movement and speech and much of patients' quality of life. Yet even with the second most common neurological disorder after Alzheimer's the diagnosis in initial stages and with a high level of accuracy still remains an issue. Exploiting speech and gait data offers tremendous potential for increasing diagnostic precision of the used tools. This research aims at incorporating the use of complex self-organising techniques and Feature selection as a means of improving early diagnosis of PD. Through the study of both speech and gait data, we employ an array of classifier models, including Random Forest, SVM, and Gradient Boosting, and use SHAP and LIME for feature importance determination. Such a model not only discriminates as to whether a person develops Parkinson's disease or not with very high accuracy but also identifies which features are most crucial in that decision. Multimodal's approach along with the strong features selection and classification leads to enhancement of the early and accurate diagnosis for Parkinson's disease which enables an early critical interferences of the patients.

关键词： Neurological diseases Support vector machines Accuracy Additives Speech analysis Parkinson's disease soft sensors Feature extraction Random forests Gait recognition

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international conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021

International Conference on Geotechnical challenges in Minin...

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international conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021

ISBN: (纸本)9789811697692

The proceedings contain 55 papers presendted at a virtual meeting. The special focus in this conference is on Geotechnical challenges in Mining, Tunneling and Underground structures. The topics include: Effect of Firing pattern on Rock Fragmentation in Surface Mine Blast-A Case Study;challenges for Mechanized Tunnelling in Urban Areas in Hanoi Vietnam;review of Roof Skin Instability Challenges with Emphasis on South African Coal Mines;stability Analysis of a Rectangular Tunnel in Granitic Rocks at a Depth of 205 m, Bhima Basin, Karnataka;geoenvironmental Assessment of Soil Slopes Around Leh Manali Highway in Himachal Pradesh, India;construction Approch of Longest Railway Tunnel of India;geotechnical Challenges in World Record Making Mine Rescue;development of Architecture of Autonomous Hydraulic Rock Breaker for Limestone Mines;utilizing a Bagging Model Based on Decision Trees and k-nearest Neighbors for Predicting Slope Stability in Open Pit Mines;robots in Mining;assessment of Structural Damage Due to Blasting in Hydro Power Tunnel;appropriate Support Design for Incline Lip Opening and Drivages in Fragile Rock Mass Formation: An Empirical and Numerical Based Case Study;application of Slope Mass Rating and Kinematic Analysis Along Road Cut Slopes in the Himalayan Terrain;fracture Characterization Using Downhole Camera in Deep Boreholes at Southern Johor Bahru, Malaysia;excavation Assessment on Granitic Area at Ulu Kinta, Perak, Malaysia for an Earthwork Project;a Review of soft computing Techniques in Predicting Overbreak Induced by Tunnel Blasting;surge Shaft Instrumentation and Monitoring in Himalayan Hydroelectric Projects;drill and Blast Optimisation at an Underground Copper-Gold Mine.

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