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the Societal Implications of False Positives Using machine learning 6th

The Societal Implications of False Positives Using Machine L...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Dubey, Anurag Mishra, Aryan Tayal, Divyansh Yadav, Aarush Jamshed, Aatif Department of IT ABES Engineering College AKTU Lucknow India Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9789819734412

the article aims to investigate the societal consequences of false positive detection through a systematic review leveraging ML (machine learning) techniques. It seeks to assess the impact of inaccurate detections on various aspects of society, such as healthcare, security, and privacy, to inform better decision-making and policy development. the study employs a comprehensive dataset encompassing diverse sources and topics to train and validate the model's efficacy. Results showcase promising outcomes, indicating that sentiment analysis can effectively contribute to the identification of misleading information. the proposed approach not only demonstrates robust performance in distinguishing between genuine and fake news but also provides insights into the emotional underpinnings of deceptive content. this research contributes to the evolving field of fake news detection, offering a nuanced perspective that aligns sentiment analysis with the imperative of fostering a more informed and resilient information landscape. the purpose of this work is to propose a notion and methods for identifying misinformation. the process of gathering news items and then utilizing SVM (SVM(Support Vector machine)) to categorize them as either authentic or fake is accomplished through the utilization of ML (machine learning) and natural language-processing processes. Comparing the results of the proposed model with those of the existing models is the purpose of this comparison. With an accuracy rating of 93.6%, the model that was provided is performing efficiently and accurately identifying the outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Different Cryptocurrencies’ Transaction Forecasting Using machine learning 6th

Different Cryptocurrencies’ Transaction Forecasting Using M...

引用

6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Aldo Stalin, J.L. Inthumathi, S. Department of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9789819986279

With the increasing geopolitical and economic issues in recent years, traditional currencies and stock markets have been facing challenges, causing global currency values to fall and investors to lose wealth. In this context, digital currencies, particularly cryptocurrencies, have gained significant attention due to their stable performance in the market. this article introduces an innovative method for creating a cryptocurrency transaction forecast model, DL forecast, using deep neural networks for learning cryptocurrency transaction network representations. the objective of this research is to investigate the potential of deep learning techniques in forecasting cryptocurrency transactions, detecting illicit transactions, and identifying interesting transaction behaviors. To achieve this aim, we used Python to develop the DL forecast model and trained it on a large dataset of cryptocurrency transactions. the model achieved an accuracy score of over 98% on the test dataset, demonstrating its effectiveness in forecasting cryptocurrency transactions. Furthermore, we showed that the DL forecast model can be employed for the purpose of identifying specific intriguing transaction patterns, overseeing valid transactions, and recognizing unauthorized entities within the realm of cryptocurrency. the results of this study have significant implications for the cryptocurrency industry, providing insights into the practical applications of deep learning techniques in the domain. the DL forecast model can be utilized by regulators, exchanges, and law enforcement agencies to improve monitoring and detection of illicit activities, such as those conducted on dark marketplaces, ransom ware operators, and fraudsters. Additionally, the model can assist in identifying legitimate transactions and behaviors, such as those conducted by regulated exchanges, merchants, and wallet services. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Analysis of Discrete Wavelet Spectra of Broadband signals 6

Analysis of Discrete Wavelet Spectra of Broadband Signals

引用

6th international Workshop on Computer Modeling and Intelligent Systems, CMIS 2023

作者： Oliinyk, Olha Taranenko, Yuri Lopatin, Valerii College of Radio Electronics st. Stepan Bandera 18 Dnipro49000 Ukraine Сompany «Likopak» st.Kachalova 1 Dnipro49005 Ukraine Poljakov Institute of Geotechnical Mechanics of the National Academy of Sciences of Ukraine st. Simferopolska. 2a Dnipro49005 Ukraine

the article is devoted to the development of a mathematical model for autoregressive and autocoherent analysis of discrete wavelet spectra with approbation on known experimental data. the model is used to detect anomalies or emergency states of the object after wavelet filtering of noise and zero crossing. the proposed method for processing noisy signals, combining noise filtering and Mahalanobis proximity analysis, shows better machine learning results than neural networks and other methods for detecting classification and recognition anomalies. the wavelet spectra were processed using autoregressive and autocoherent analyses. the possibility of using these functions as classification features for identifying possible anomalous states of devices is shown. Before forming the wavelet spectra, the procedure of direct with filtering and inverse transformation of the already filtered signal is carried out. As a result, it seems possible to identify the characteristic features of the signal - anomalies or emergency conditions. the main factor in the classification of device status signals is the moment when changes in the signal spectrum begin to develop, therefore, all analysis, both in the time and frequency domain, is tied either to the number of samples by the time the signal was received, and in some cases even to the extended date format - month number. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： machine learning

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Certain investigations on Computed Tomography based imaging for identification of lung cancer 6

Certain investigations on Computed Tomography based imaging ...

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6th international conference on Advanced Computing and Communication Technologies for High Performance Applications (ACCthPA)

作者： Vijayan, Nishiya Kuruvilla, Jinsa APJ Abdul Kalam Technol Univ Mar Athanasius Coll Engn Dept ECE Kothamangalam Kerala India

ISBN: (纸本)9798350398441

In terms of mortality, lung cancer ranks second worldwide among diseases with the greatest danger. Although early diagnosis might improve recovery, it is a very hectic task in its early stage. With the help of computer-assisted diagnostics, lung cancer can be detected in its early stages. Most procedures rely on computed tomography (CT) scans for diagnosis as it is cheaper, more flexible to capture the image, are quickly available, and have less distortion. the CT images captured with low tube current result in noise in the captured CT image. Before classification, various image pre-processing techniques are frequently employed to minimize the impact of noise in the lung CT image. the features of the pre-processed images are then used for predicting lung cancer. the various stages of a lung cancer diagnosis are thoroughly analyzed in this paper using machine learning and deep learning techniques. Also discussed a number of features that relate to classification.

关键词： Pre-processing Segmentation Feature extraction Classification Performance metrics CT scan

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Heterogeneous Stacked Ensemble Framework for Surface Electromyography signal Classification 10th

Heterogeneous Stacked Ensemble Framework for Surface Electro...

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10th Biennial international conference on Pattern Recognition and machine Intelligence (PReMI)

作者： Samui, Suman Garai, Soumen Ghosh, Anindya Mukhopadhyay, Anand Kumar Natl Inst Technol Durgapur Durgapur India Indian Inst Engn Sci & Technol Shibpur Howrah India MathWorks India Private Ltd Bangalore Karnataka India

ISBN: (纸本)9783031451690;9783031451706

Surface electromyography (sEMG) signal is essential for accurately controlling prosthetic devices with numerous degrees of freedom in human-machine interfaces for robotics and assistive technologies. the controlling method of the upper-limb prosthesis device depends on electromyogram (EMG) pattern recognition, which requires the efficient blending of conventional signal processing and machine learning. this paper focuses on stacked ensemble models, one of the popular methods for reducing generalization error. the proposed work uses a dataset of sEMG signals from different upper-limb positions in subjects. the raw signals are transformed into correlated time-domain descriptors (cTDD) for feature extraction, which are then used to train the stacked ensemble framework. the framework includes four base classifiers (support vector machine (SVM), K-nearest neighbours (KNN), logistic regression (LR), and decision tree (DT)) and two meta-classifiers (random forest (RF) and multi-layer perceptrons (MLP). the performance of the meta-classifiers is evaluated on two test sets, showing superior classification accuracy compared to the basic classifiers. the proposed approach demonstrates the capability to accurately classify limb position invariant EMG signal classification for prosthetic device control.

关键词： sEMG signal classification Stacking ensemble learning Myoelectric control Human-computer interaction Supervised learning

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Monitoring of Parkinson's Disease Progression based on Speech signal 6

Monitoring of Parkinson's Disease Progression based on Speec...

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Hemmerling, Daria Wojcik-Pedziwiatr, Magdalena Jaciow, Pawel Ziolko, Bartosz Igras-Cybulska, Magdalena AGH University of Science and Technology Krakow Poland The John Paul II Hospital Department of Neurology Krakow Poland Andrzej Frycz Modrzewski Krakow University Department of Neurology Krakow Poland

ISBN: (纸本)9798350300956

the goal of this study was to determine whether the speech analysis might be useful tool to track the motor symptoms of patients with Parkinson's disease during the levodopa treatment with reference to the Unified Parkinson's Disease Rating Scale (UPDRS). Due to speech impairments caused PD we carried out the acoustic analysis based on automatic speech recognition system to extract vowels and consonant from speech signals to indicate the UPDRS score in an automatic manner. Speech analysis was the input to Support Vector machine, Support Vector Regression and Gaussian Process Regression algorithms to determine UPDRS score. the lowest mean absolute error of UPDRS score achieved in this study is 8.52 points (\rho =0.72, r=0.69) when the analysis was carried out using only vowels extracted from speech signal. the developed method may help to follow the patient's speech fluctuations and adjust the treatment. © 2023 IEEE.

关键词： Speech processing

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A Novel Brain Connectivity-Powered Graph signal processing Approach for Automated Detection of Schizophrenia from Electroencephalogram signals 10th

A Novel Brain Connectivity-Powered Graph Signal Processing A...

引用

10th Biennial international conference on Pattern Recognition and machine Intelligence (PReMI)

作者： Pain, Subrata Vimal, Naincy Samanta, Debasis Sarma, Monalisa Indian Inst Technol Adv Technol Dev Ctr Kharagpur 721302 India Indian Inst Technol Dept Comp Sci Kharagpur 721302 India Indian Inst Technol Subir Chowdhury Sch Qual & Reliabil Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

Schizophrenia is a severe neural disorder that affects around 24 million individuals globally. In this context, Electroencephalogram (EEG) signal-based analysis and automated screening for Schizophrenia (SZ) have gained importance. EEG-based Schizophrenia (SZ-EEG) analysis is traditionally done by extracting features from individual EEG electrodes' signals and utilizing these features for machine learning (ML)-based classification models. However, these methods do not exploit the Schizophrenia-induced alteration of functional brain connectivity between neuronal masses. the present study proposes a novel graph-signal (GS) representation of multi-channel SZ-EEG data that fully encompasses local brain activation and global interactions between brain regions. the proposed GS representation comprises the underlying connectivity network and the signal values on the network's vertices. Here, the EEG signal's entropy at each electrode is used as GS values, and a phase lag index (PLI)-based functional connectivity measure is utilized as the underlying connectivity network. Further, these connectivity-informed GSs are transformed to the spectral domain by the Graph Fourier Transform (GFT), and relevant discriminatory features are extracted from them using the Graph signal processing (GSP) technique. those features are fed to basic ML-based classification models. the efficacy of the proposed PLI-GSP framework is validated using a publicly available SZ-EEG dataset, and a 99.77% classification accuracy is achieved that outperforms most of the state-of-the-art models.

关键词： Schizophrenia Electroencephalogram signals Graphical Representations Graph signal processing machine learning

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Survey on Driver Fatigue Detection Using Sensors, Big Data Analytics and machine learning Techniques 6th

Survey on Driver Fatigue Detection Using Sensors, Big Data A...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Deshmukh, Ganesh Khuspe, Aditi Kadam, Radha Kamble, Anjali Phalke, Aditi Pimpri Chinchwad College of Engineering Pune India

ISBN: (纸本)9789811935701

Fatigue detection in today’s era has become an important asset as the death rate of India is at its peak, and the reason for it is road accidents. It is very important to identify driver’s drowsiness in the early stages, for minimizing the damage and preventing accidents. Drowsiness can be detected considering parameters like facial expressions, human physiological signals and vehicular parameters. It is possible to detect the state of driver’s fatigue with the development of the technology of IoT, machine learning and big data analytics. this paper focuses on all the techniques and processes involved in the fatigue detection, which includes all the sensors that can be used like heartbeat sensor, temperature sensor, eye blink sensor, etc. As the data collected from sensors is huge, so it can be termed as big data, and for processing this data, big data and machine learning techniques are used which are discussed here. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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Cognitive Controller for 6G-Enabled Edge Autonomic 14

Cognitive Controller for 6G-Enabled Edge Autonomic

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and the 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Rahmani, Rahim Firouzi, Ramin Sadique, Kazi Masum Department of Computer and Systems Sciences Stockholm University Sweden

this article proposes a new Artificial Intelligent (AI) and machine learning (ML) based framework for 6G-enabled Intelligent edge computing. the framework will be equipped with multiple cognitive controllers to harmoniously control various aspects in distributed intelligence toward edge nodes collaboration. Autonomic cognitive controller for edge computing is a popular computing paradigm where the distributed metadata processing and edge intelligence are performed at edge node in 5G/6G network for management, connectivity and interoperability. Some of studies focused on edge management improvement such as reduce the response time and bandwidth costs. However, the previous approaches are inadequate to support autonomously management for large-scale deployment for connectivity for dynamic and reliable communication. We propose a cognitive controller for edge autonomy and collaboration application development. Finally, we discuss challenges and open issues toward cognitive controller and distributed edge intelligence. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Element Selection with Wide Class of Optimization Criteria Using Non-Convex Sparse Optimization 48

Element Selection with Wide Class of Optimization Criteria U...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Kawamura, Taiga Ueno, Natsuki Ono, Nobutaka Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Tokyo Hino-shi191-0065 Japan

ISBN: (纸本)9781728163277

Element selection techniques for high-dimensional features have various applications in machine learning. In general, the problem of element selection is typically solved by greedy methods or convex relaxation methods. However, these algorithms are applicable to only a specific class of optimization criteria such as the minimization of the squared error loss between the original and restored data. To overcome this limitation, we propose an element selection algorithm based on non-convex sparse optimization that can be used with a wider class of optimization criteria than conventional algorithms. In the proposed method, an algorithm based on the alternating direction method of multipliers (ADMM) is de-rived by reformulating the element selection problem as a matrix optimization on a non-convex set. A numerical experiment demonstrated the effectiveness of the proposed method for element selection with a non-squared error loss compared with the conventional greedy method. © 2023 IEEE.

关键词： ADMM Dimensionality reduction element selection proximal operator sparse optimization

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