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A parametric survey on polyphonic sound event detection and localization

Multimedia Tools and Applications 2024年 1-38页

作者： Mohmmad, Sallauddin Sanampudi, Suresh Kumar JNTU Hyderabad India School of Computer Science and Artificial Intelligence SR University TS Warangal India Department of Information Technology JNTUH University College of Engineering Nachupally Kondagattu TS Jagtial India

With the rapid growth in technology, everything will become automatic. Automatic detection is needed in health, security, smart homes, smart cities, and various environments. Sound is an essential function of automatic detection, which can be created in different environmental situations. To handle this, various sound detection devices are incorporated that run alone or with a combination of video. This auditory detection system is becoming more emerging in research today. This study is a survey on sound event detection and localization-related models. This survey research included details about different datasets, feature extractions, and classification methods that are implemented in sound-related research. Distinct and typical environmental situations comprise multiple sound sources that generate different sounds and noise. Our research compares distinct algorithms to detect sound events and localization, such as HMM, CNN, SVM, Random Forest, NMF, CRNN, VGG16, etc. Sound-related feature extraction methods, such as MFCC, Mel-Spectrogram, Mel band elements, ZCR, and wavelet features, and their importance in sound classification are also explained with a comparative analysis approach. The sections end with challenges with the existing approaches in different environments and feature extraction combinations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WITH BALANCING DATA TECHNIQUE

FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WIT...

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2023 IET International Conference on engineering Technologies and Applications, ICETA 2023

作者： Damanik, Nurafni Liu, Chuan Ming College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Science National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781839539848

In today's world, online payment has become the most popular transaction method, making payments convenient for people. However, this ease of use comes with the risk of an increasing number of online fraud incidents. Conventional rule-based systems and static fraud detection approaches often struggle to keep up with the ever-evolving tactics of fraudsters. Consequently, there is a growing need for advanced fraud detection methods that can adapt to dynamic fraud patterns, which presents both importance and challenges. This study proposes a fraud online payment detection system based on Logistic Regression and Decision Tree algorithms, enhanced through the use of oversampled data techniques. The experimental evaluation reveals that the Decision Tree, when utilizing the random over-sampling (ROS) technique, outperforms other techniques in terms of accuracy, achieving a remarkable 100% accuracy rate. © The Institution of engineering & technology 2023.

关键词： Decision trees

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Continuous Wavelet Transform Spectral Image Based Epileptic Episode Classification 25

Continuous Wavelet Transform Spectral Image Based Epileptic ...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Nuhi-Alamin, Md. Sarkar, Ajay Krishno Talukder, Md. Simul Hasan Aljaidi, Mohammad Swapnil, Safkat Shahrier Rajshahi University of Engineering & Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh Dhaka University of Engineering & Technology Department of Electrical and Electronic Engineering Gazipur Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331540012

Numerous individuals globally, regardless of age, have the neurological condition epilepsy. Recurrent seizures compromised motor and sensory abilities, and a hindered normal lifestyle are all signs of epilepsy. By seeing or manually examining electroencephalogram (EEG) data, neurologists can make the diagnosis of epileptic seizures. In this paper, a continuous wavelet transform spectral image-based epileptic episode classification technique is proposed. The EEG signal was first converted into 227x227 RGB images using CWT. Then these images were used to create an image dataset, and classification was performed using the proposed deep ensemble epileptic seizure classifier (DE-ESC). DE-ESC ensembled Dense-net-169, Dense-net-201, and Inception-V3. This proposed method obtained 100% accuracy (Ac), precision (Pr), recall, and F1 Score, respectively. © 2024 IEEE.

关键词： Wavelet transforms

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Mitigating Cyber Threats in Critical Infrastructure: A Technical Analysis 15

Mitigating Cyber Threats in Critical Infrastructure: A Techn...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunkumar, D.T. Agarwal, Trapty Upadhyay, Ramakant School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

This technical analysis research the techniques and methods for mitigating cyber threats in essential infrastructure which include the electricity grid, transportation, and the water supply. It discusses the benefits of risk interception, the identity of attack strategies, and the implementation of countermeasures. The evaluation additionally provides perception into the significance of comfy network architecture, dependable patch management, and effective statistics safety regulations. Additionally, the paper considers latest advances in artificial intelligence, system gaining knowledge of, and block chain era, and the way these improvements may be used to bolster normal protection. Particular case studies illustrate the effectiveness of these solutions and highlight the importance of chance control and proactive safety features. In the long run, this analysis highlights the paramount need for vigilance and complete protection controls to ensure the integrity and stability of important infrastructure. © 2024 IEEE.

关键词： Critical infrastructures

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Blockchain Based Traceability in computer Peripherals in Universities Scenarios 3

Blockchain Based Traceability in Computer Peripherals in Uni...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Kumar, Jakka Raja Harshit Bhargavramu, Nettem Durga, Lella Saketh Naga Nimmagadda, Dinesh Bhattacharjya, Aniruddha Koneru Lakshmiah Education Foundation Computer Science Engineering Vijayawada India Sgt University Faculty of Engineering & Technology Department of Computer Science Engineering Gurugram India

ISBN: (纸本)9798350327762

Tracing and tracking of the peripheral devices help in terms of manufacturing and providing services to the users. However, most of the current systems which are suggested by Food and Drug Administration (FDA) are loT and Hardware-Assisted Tracing data which are being stored as required. The time and effort on duplicating record keeping and third-party validations are wasting of time and effort towards verification process. Record-keeping systems can be attacked by fraud and cyber-attacks whereas Blockchain is ideal for delivering that information securely and in a distributed fashion. This research has proposed a Blockchain based traceability of the computer Peripherals in Universities scenarios. It is well-known that the Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. Smart contract is a good feature for maintaining this kind of peripheral data. In addition to providing quality information, the proposed traceability system enables transparency, safety, and security. We have done security analysis also. © 2023 IEEE.

关键词： Blockchain

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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest X-rays machine learning

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The Assessment of the Design Risk for ORAN

The Assessment of the Design Risk for ORAN

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shiang-Jiun Chang, YaHan Raksasook, Nattapol Wu, Wei-Liang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology International Electrical Engineering and Computer Science Taipei Taiwan University of Taipei Department of Mathematics Taiwan

ISBN: (纸本)9798350324174

The Open Radio Access Network (O-RAN) uses a virtualization architecture to disaggregate components and connect them through open interfaces, and also uses software defined networking (SDN) technology for decoupling. The O-RAN architecture is an innovative telecommunication architecture, which also brings some potential problems for security, including but not limited to the followings: new architecture (disaggregation, Real Time RAN intelligent controllers (Near-RT RIC), virtualization), the open interfaces (O1, A1, and E2, etc.), artificial intelligent and machine learning (AIML) workflow, and standardization issues. Facing the new structure, in addition to the original risks, new risks also need to be considered. "Secure in design"is imperative to mitigate the information security risks. This study will look at the risk assessment of O-RAN from the perspective of testing and do security analysis. © 2023 IEEE.

关键词： Machine learning

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Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

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SN computer science 2024年第8期5卷 1-10页

作者： Sonavale, Amruta Chakkaravarthy, Midhun Srinivasa Rao, Surampudi Salleh, Hishamuddin Bin M. Jadhav, Jagannath Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia ECE Malla Reddy College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering KLE College of Engineering and Technology Chikodi India

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is *** process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The propos

关键词： Adaptive median filtering Flood detection LSTM Neural network algorithm Structural object son the RSI Versatile Linear Edge Detection (VLED) algorithm

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Automizing computer Vision Systems with Deep Learning Techniques 3

Automizing Computer Vision Systems with Deep Learning Techni...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Saraswat, Pankaj Pavan, P.S. Chaudhary, Chetan Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350354171

Deep getting-to-know techniques have been used to automate PC imaginative and prescient systems during the last few years with terrific fulfillment. Deep mastering is a subset of the system gaining knowledge of that uses synthetic neural networks, a set of synthetic neurons that paintings collectively to simulate the biological neurons of a human mind. Through using deep learning, a synthetic neural network is skilled to understand patterns from pics or videos, which can then be used to understand gadgets in pictures or to song changes over time. Because of the big potential for automation in computer imaginative and prescient structures, deep getting-to-know techniques are being actively researched and developed by way of each instructional and industrial team. Presently, deep getting-to-know networks are used in a spread of PC imaginative and prescient programs, which include facial reputation, item recognition, and video object *** to the modern methods, studies into the development of extra-effective techniques are ongoing. This research is focused on growing algorithms that could perform nicely in one-of-a-kind environments, as well as the development of new architectures and different parameters that could accelerate schooling overall performance. A lot of those new processes involve the use of statistics-driven fashions along with convolutional neural networks. With the aid of the use of deep studying techniques to automate PC imaginative and prescient structures, the capacity for automated programs in fields that include surveillance, medical imaging, and self-reliant cars is massive. © 2024 IEEE.

关键词： Convolutional neural networks

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