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International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

作者： Abdul Quaiyum Ansari Priyanka Sharma Manjari Tripathi JamiaMilliaIslamia New Delhi India All India Institute of Medical Sciences New Delhi India

ISBN: (数字)9781728183374

ISBN: (纸本)9781728183381

Seizures can be classified into convulsive and non-convulsive type. First type of seizures, viz., convulsive oneis easy to be diagnosed due to the physical symptoms that are associated with it. Second type of seizures, also called absence seizures is hard to be detected and hence treated. This study focuses on designing of an automatic detection algorithm for absence seizures. Approximate entropy and Energy present in the Delta sub-band of electroencephalogram (EEG), are used as features for classification of normal and absence seizures using a simple linear classifier. When tested on the scalp EEG of two patients having five seizures, the sensitivity and specificity of 98.46% and 99.86% have been obtained by the proposed algorithm. An online detection system can be implemented using the proposed method once it is tested on a large dataset.

关键词： Sensitivity Sensitivity and specificity Feature extraction Entropy Electroencephalography Classification algorithms detection algorithms

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Unveiling the Deception: Understanding the Urgent Need to Combat Deep Fake Videos

Journal of Computing Sciences in Colleges

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Journal of Computing Sciences in Colleges 2024年第3期40卷

作者： Jada A. Danner Computer Science Department Bowie State University Bowie MD USA

The exponential growth of deep fake technology presents a profound threat to the fabric of modern society, undermining trust in digital content and eroding the foundations of truth and authenticity. In this study, we delve into the motivations driving the creation and dissemination of deep fake videos, recognizing the urgent imperative to address this pressing global challenge. By dissecting the intricate web of incentives behind deep fake production, including political manipulation, fraud, and misinformation campaigns, we elucidate the farreaching consequences for democratic processes, public discourse, and individual privacy. The proliferation of deep fake videos exacerbates existing societal fissures, perpetuating disinformation, and sowing discord in an already polarized world. Moreover, we underscore the critical need for proactive measures to counteract the pervasive influence of deep fakes, advocating for interdisciplinary collaborations between policymakers, technologists, and civil society. Through the development of robust detection algorithms, enhanced media literacy initiatives, and regulatory frameworks, we can safeguard the integrity of digital content and fortify societal resilience against emerging threats. This study serves as a clarion call to action, highlighting the imperative of collective vigilance and concerted efforts to mitigate the deleterious impacts of deep fake videos. By fostering a deeper understanding of the motivations driving this phenomenon, we pave the way towards a more resilient and trustworthy digital ecosystem, preserving the integrity of information in an increasingly interconnected world.

关键词： deep fake technology detection algorithms digital trust erosion disinformation campaigns media literacy initiatives

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Foreground detection Algorithm via Fusing Confidence by Weight and Visual Attention

Foreground Detection Algorithm via Fusing Confidence by Weig...

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Internet of Things and Intelligent Applications (ITIA), International Conference on

作者： Cheng Keyang Wu Jinxia Liu Qing Sun Shuang School of Computer Science and Telecommunications Engineering Jiangsu University Zhenjiang China National Engineering Laboratory for Public Safety Risk Perception Control by Big Data Zhenjiang China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

In the foreground detection of video, the result of pixel-level background subtraction is clear and flexible. However, the pixel-level classification method based on sample consistency can't make full use of the pixel information effectively and get full foreground mask when meeting the complex situation of color camouflage and static object. In order to solve this problem effectively, we proposed an algorithm based on fusing confidences with weight and visual attention. Firstly, the foreground is determined by the sum of fusing with color confidence and texture confidence based on weight, then the confidence and weight of the samples are updated adaptively; the static foreground is determined by constructing the visual attention mechanism in the subsequence, and finally the background model is dynamically updated with the strategy of updating the minimum confidence samples. The method proposed in this paper is tested on the open dataset. It is found that the proposed algorithm is superior to the contrast algorithms, and get the accuracy and recall rate of foreground detection in the complex situation of color camouflage and still objects, and get better detection effects.

关键词： Image color analysis Adaptation models Visualization Color detection algorithms Telecommunications Computer science

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A New Feature Selection Method for Intrusion detection System Dataset – TSDR method

A New Feature Selection Method for Intrusion Detection Syste...

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International Conference on Computational Intelligence and Security

作者： Tao Yu Zhen Liu Yuaning Liu Huaibin Wang Nodir Adilov Graduate School of Engineering Nagasaki Institute of Applied Science Japan College of Computer Science and Technology Jilin University China Technical College of Deaf Tianjin University of Technology China Purdue University Fort Wayne USA

In recent years, due to the increased frequency of cyber-attacks, the negative impacts of cyber-attacks on society have increased. Therefore, the research on cyber-security and prevention of cyber-attacks, including intrusion detection as an effective means of defense against cyber-attacks, is warranted. Both in the research and in the development of the systems for intrusion detection, the machine learning and deep learning methods are widely utilized, and the NSL-KDD dataset is frequently used in algorithm research and verification. In this paper, we propose a new two-stage dimensionality reduction (TSDR) feature selection method and verified by NSL-KDD dataset. The method reduces the dimensionality of the dataset and significantly improves the calculation efficiency. The KNN algorithm is used to verify that the new feature selection method improves the calculation efficiency. The accuracy rate is only slightly reduced when compared to the full feature calculation.

关键词： Dimensionality reduction Deep learning Machine learning algorithms Intrusion detection Feature extraction Security detection algorithms

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Smart Parking System Using MobileNet and Single Shot detection Algorithm and IoT

Smart Parking System Using MobileNet and Single Shot Detecti...

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International Conference on Electrical and Computer Engineering (ICECE)

作者： Galib Ibne Haidar Hasin Ishraq Reefat Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

In today's world, automobiles are essential part of our day to day lives and people who drive regularly can certainly say that parking is the most difficult and exhausting part of driving. Finding a parking spot in big cities is another strenuous assignment. As this is the era of remarkable technologies, this paper proposes a smart parking system with the help of IoT (Internet of Things) and artificial neural network to ease off the fatigue of the drivers.

关键词： Computational modeling Software algorithms Object detection Software Internet of Things detection algorithms Python

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A Deep Learning-Based Method for Automatic detection of Epileptic Seizure in a Dataset With Both Generalized and Focal Seizure Types

A Deep Learning-Based Method for Automatic Detection of Epil...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： A. Einizade M. Mozafari S. Hajipour Sardouie S. Nasiri G. Clifford Sharif University of Technology Tehran Iran Emory School of Medicine Georgia USA Georgia Institute of Technology Georgia USA

ISBN: (数字)9781728188201

ISBN: (纸本)9781728188218

Epilepsy is the second most popular neurological disorder affecting 65 million people around the world. Seizures are classified into two kinds; focal and generalized ictal activities, reflecting the spread of seizure activity on the brain. Focal seizures start and affect specific regions of the brain, whereas the generalized propagates throughout the brain. Current approaches to developing an automatic seizure detection algorithm do not consider the types of seizures. However, to detect the focal seizures, the locations of onset of seizure must be identified by an expert through inspection of the electroencephalogram (EEG), which is an expensive and time-consuming procedure. Moreover, most proposed methods are patient-specific and cannot be generalized on an unseen patient, limiting the clinical usage of previous studies. This work presents a generalizable seizure detection algorithm by considering different seizure types. After pre-processing data and rejecting artifacts, a deep neural network is used to extract robust representations across seizures and a population. The proposed method includes deep recurrent and convolutional neural networks to capture spatial and temporal information simultaneously. Experiments on the TUH EEG seizure dataset, which contains both generalized and focal seizures, show that the proposed method increases the accuracy over state-of-the-art from 80.72% to 82%, precision from 67.55% to 71.69%, and sensitivity from 80% to 85%.

关键词： Neurological diseases Sensitivity Sociology Electroencephalography Noise measurement detection algorithms Statistics

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CNN-based Speed detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors

CNN-based Speed Detection Algorithm for Walking and Running ...

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International Conference on Smart Computing (SMARTCOMP)

作者： Venkata Devesh Reddy Seethi Pratool Bharti Computer Science Northern Illinois University Dekalb USA

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common ones people do in everyday life, either through commute, exercise, or doing household chores. If done at the right intensity, walking and running are sufficient enough to help individual reach the fitness and weight-loss goals. Therefore, it is important to measure walking/ running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with high precision. Data from 15 participants were collected while they were walking/running at different speeds on a treadmill. Our speed detection algorithm achieved 4.2% and 9.8% MAPE (Mean Absolute Error Percentage) value using 70-15-15 train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.

关键词： Legged locomotion Neural networks Particle measurements detection algorithms Surges Wearable sensors Injuries

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Series Dc Arc Characterization, Prevention & detection Inaircraft Systems

Series Dc Arc Characterization, Prevention & Detection Inair...

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作者： Eric Charles Bauer The Ohio State University

学位级别：硕士

In airplane power distribution systems, dc bus voltages are expected to rise aselectrication efforts push power demands higher. Higher bus voltages improve systemeffciency and reduce the weight of the cabling system. Despite these advantages,electric arcs become a signicant threat in aircraft when the higher voltage is operatedat altitude. Arcs can occur either in series or in parallel with a load. Parallelarcs become near short circuits that release immense energy and pose a severe hazardto aircraft. The onboard Fault Management System (FMS) needs to be able to reactquickly and accurately to these high energy faults to ensure system *** arcs also need to be detected and isolated by the FMS, but the additionalseries arc impedance results in a load current decrease. Without proper detectionmethods, this fault can be mistaken for a load transient since the current still fallswithin normal operating limits. This thesis gives an overview of the key componentsand objectives of a Fault Management System. This includes an introduction to variousfault detection algorithms, fault isolation schools of thought and recongurationstrategies used in modern microgrids. Further focus will be given for how the FMSmust handle the series arc fault at each stage of the arc timeline. First, series arcmodeling is discussed. Impedance-based prevention is then proposed by utilizing anovel arc energy accumulation mechanism to estimate the critical re-ignition ***, optimal detection threshold selection and effcacy will be addressed.

关键词： Electrical Engineering series arc series dc arc arc prevention arc modeling arc characterization arc detection more electric aircraft steady state model series arc transient energy accumulation detection algorithms

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Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images

Using Machine Learning to Detect Rotational Symmetries from ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Koen Ponse Anna V. Kononova Maria Loleyt Bas Van Stein LIACS Leiden University Leiden The Netherlands Ornamika Moscow Russia

ISBN: (纸本)9781728190495

Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. For one of such algorithms aimed at reflectional symmetries, we propose postprocessing improvements to find localised symmetries in images, improve the selection of detected symmetries and identify another symmetry type (rotational). In order to detect rotational symmetries, we contribute a machine learning model which detects rotational symmetries based on provided reflection symmetry axis pairs. We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.

关键词： Computer vision Machine learning algorithms Art Computational modeling Reflection Classification algorithms detection algorithms

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Performance Improvement of Energy detection in Cognitive Radio Under Noise Uncertainty

Performance Improvement of Energy Detection in Cognitive Rad...

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International Conference on Innovations in Information Technology

作者： Morteza Alijani Anas Osman University of Trento Trento Italy

ISBN: (数字)9781728181844

ISBN: (纸本)9781728181851

Spectrum scarcity has been one of the most conspicuous constraints in networking, amidst the growing demand for 5G and Internet of things (IoT) services. Consequently, a cognitive radio networking system (CRN) presents as a reliable solution to counter the current spectrum limitations. Many spectrum sensing techniques have been integrated with CRN to address the spectrum shortage; through the detection of the frequency band and investigating the status of the primary user (PU). Nonetheless, energy detection approaches are perceived as one of the most effective solutions for spectrum sensing, as they can operate in a non-causal manner. Energy detection works with a single or double threshold. Each threshold plays a fundamental role taking into account the stochastic properties of the detected signal and consequently the calculation of the optimal energy level. Despite various research efforts towards improving the energy detection scheme, the proposed algorithms still struggle in coping with noise uncertainty and signal-to-noise ratio (SNR) wall. The main contribution of this paper is the implementation and comparison between a single (conventional) and double energy detection algorithms, in terms of the characteristics, performance, and simulation results. Finally, we analyze the noise effect on the energy detection approach and present opportunistic potential solutions such as Machine learning (ML) and Graph signal processing (GSP) techniques.

关键词： Sensors Uncertainty Signal to noise ratio Cognitive radio Technological innovation Information technology detection algorithms

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