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Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data

Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Mod...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Nowshad Hasan Md. Maskat Sharif Miskatur Rahman Md Saiful Islam Md Tohedur Rahaman Khan MD Jiabul Hoque Anamul Bahar Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science Enginnering International Islamic University Chittagong Chittagong Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Enginnering University of London London United Kingdom

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating characteristics of EEG data. To overcome these difficulties, this research employs a specialized CNN-GAN hybrid model designed to improve seizure prediction by generating realistic synthetic data. This approach addresses class imbalance and enhances feature learning. The process begins with preprocessing EEG datasets to remove noise and artifacts. Data augmentation follows, expanding the dataset and reducing the risk of overfitting. Segmentation then isolates important features to boost prediction accuracy. Feature extraction is achieved using a custom CNN model, and classification is performed with a GAN model. The model demonstrates high accuracy, with results of 99.95% on the BONN Dataset, 98.93% on the CHB-MIT Dataset and 98.80% on the SWEC-ETHZ Dataset. Overall, the custom CNN-GAN hybrid model significantly improves the accuracy of predicting epileptic seizures from EEG signals across various datasets.

关键词： Representation learning Accuracy Predictive models Brain modeling Data augmentation Feature extraction Electroencephalography Data models Robots Synthetic data

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Traffic Classification For Botnet Detection Using Deep Learning

Traffic Classification For Botnet Detection Using Deep Learn...

引用

international conference on Smart Cities, automation & Intelligent Computing Systems (ICON-SONICS)

作者： Md. Sagar Hossen Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

In this modern world, we are naturally dependent on technology, which is constantly evolving. At the same time, our fear of data fraud is increasing every day. The ever-increasing number of attacks on our servers indicates that people have learned to adapt to new technologies. In addition, the number of botnet attacks is not insignificant, and botnets threaten computer networks. This can impact security systems, including malware distribution, phishing, spamming, and click fraud. Due to their harmful effects, botnets must be identified as soon as possible. Due to the dynamic character of botnets, however, detection has proven difficult. In this proposed method, deep learning is used to analyze and predict botnet data, and model performance metrics such as accuracy, precision, Recall, and F1 score are compared to prior work. In the proposed procedure, patterns are also discussed by analyzing botnet data. Various scenarios and sensors separate the NCC and NCC-2 datasets. The NCC dataset is separated into 13 scenarios, while the NCC-2 dataset is separated into 3 sensors. The dataset is preprocessed using the clean method, the null value handling method, the imbalance method, and the feature selection method to extract the essential characteristics. The dataset is then trained using LSTM, GRU, and Bi-LSTM models, and 99.93% accuracy is achieved.

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TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device

TinyML Speech Classification Embedded Ai Module for Hand Reh...

引用

Mechatronics, control and robotics (ICMCR), international conference on

作者： Arkorn Numsomran Arjin Numsomran Jutarut Chaoraingern Triam Udom Suksa Pattanakarn Suvarnabhumi School Bangkok Thailand Department of Instrumentation and Control Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and AI Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Recent advancements in artificial intelligence and machine learning have significantly improved healthcare, especially in the development of assistive technologies for rehabilitation. This paper introduces a method for hand rehabilitation that utilizes the capabilities of tiny machine learning to enhance speech classification in a rehabilitation device. The proposed method employs a CNN model capable of classifying speech signals that are indicative of different hand movement patterns. Patients emit these speech signals during prescribed hand exercises, which are crucial for their rehabilitation process. The main focus of this study is on training and deploying a speech classification system that can work in the resource-limited environment of TinyML platforms. We detail the process of capturing speech data, preprocessing it, and extracting the most features relevant to different hand movements. Our results show that using TinyML to help with hand rehabilitation works. The method we came up with shows how TinyML could change the way rehabilitative devices are controlled, and it also shows us what personalized and easy-to-use rehabilitation tools might look like in the future.

关键词： Hands Training Technological innovation Tiny machine learning Medical treatment Speech recognition Transforms Speech enhancement Feature extraction Robots

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Possibilities of Using Neural Networks to Assess Speech Intelligibility

Possibilities of Using Neural Networks to Assess Speech Inte...

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informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Victor A. Trushin Ivan L. Reva Rail R. Bakhrachyov Department of Information security Novosibirsk State Technical University Novosibirsk Russia Department of Automation Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The paper is devoted to the consideration of modern speech recognition technologies for speech intelligibility evaluation. Speech intelligibility is an important criterion for evaluating the quality of communication lines in various fields (telephony, radio, information security). Currently, to assess speech intelligibility in the tasks of information security use an instrumental-calculative method based on the results of experimental research conducted by N.B. Pokrovsky. The methodology of N.B. There is also a more accurate way to assess speech intelligibility - the method of articulation. The criterion of intelligibility here is the intelligibility of speech elements (sounds, syllables, words or phrases) - a value defined as the ratio of the number of correctly received speech elements to the total number of transmitted elements. However, this method has certain disadvantages, in particular, the cumbersomeness of articulation measurements, which require an articulation team and a large amount of time. As a result, the question arises whether it is possible to eliminate these disadvantages by replacing the receiving operator with an artificial equivalent. It is necessary to experimentally evaluate the performance of modern speech recognition technologies in recognizing noisy speech with different signal-to-noise ratios.

关键词： Instruments Neural networks Information security Speech recognition Telephony Particle measurements Time measurement Noise measurement informatics Signal to noise ratio

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Design and Implementation of a Smart Wind Turbine with Yaw Mechanism

Design and Implementation of a Smart Wind Turbine with Yaw M...

引用

international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Subrotho Bhandari Abhi Md. Ismail Hossain Rezaul Hoque Suny Fatema Tuz Zahura Md. Rifat Hazari Effat Jahan Mohammad Abdul Mannan Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

In this paper, an intelligent low-cost wind turbine is designed which can rotate the rotor hub towards wind direction. The smart wind turbine technology has developed by using six rotor blades, squirrel cage induction generator (SCIG), and yaw mechanism. The yaw mechanism relates to two stepper motors through support tower. Based on the wind direction, the yaw mechanism will control the stepper motors. Finally, this stepper motors will move the wind turbine towards the wind direction. Conventionally, the hub of wind turbine is kept at fixed position. However, this kind of arrangement is not suitable where in some places (e.g. Bangladesh), the wind directions can be changed frequently. Therefore, it is necessary to move the rotor hub continuously towards the wind direction so that it can capture maximum amount of wind power. So, a smart wind turbine with yaw mechanism is designed in this paper.

关键词： Induction motors Blades Induction generators Friction Poles and towers Rotors Wind power generation

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Intelligent Algorithms for Gaming AI: Research Into Decision-Making Methods and Agent Behavior

Intelligent Algorithms for Gaming AI: Research Into Decision...

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informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Anastasia R. Popova Gleb Oh. Bondarenko Alyona S. Syryh Vladislav S. Pavluk Evgenii I. Geichenko Viacheslav D. Burlaka Faculty of Information Measuring and Biotechnical Systems Saint Petersburg Electrotechinical University “LETI” Saint-Petersburg Russia Computer Engineering Laboratory Saint-Petersburg University of Information Technologies Mechanics and Optics (ITMO) Saint-Petersburg Russia Video Game Development School Saint-Petersburg University of Information Technologies Mechanics and Optics (ITMO) Saint-Petersburg Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This article delves into the exploration of the vector method of orientation within the scope of artificial intelligence (AI) systems. The vector approach, which forms the foundation of numerous contemporary algorithms, offers significant advantages in terms of accuracy and efficiency when tackling problems related to direction and distance determination. The primary objective of the paper is to thoroughly examine the fundamental principles and algorithms that employ vector methods, while also investigating their practical applications in diverse fields, including navigation, robotics, and autonomous systems. By providing a comprehensive analysis, the article aims to uncover the potential of vector-based methods in enhancing the performance and capabilities of AI systems. Additionally, the discussion will be enriched by practical examples and scenarios illustrating how these methods can be effectively utilized in real-world situations, thereby highlighting their relevance and versatility across various domains. Special attention will be given to potential future advancements and challenges in this area.

关键词： Training Accuracy Navigation Scalability Games Vectors Recording Artificial intelligence informatics Rescue robots

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Design and Implementation of Smart Wheelchair with Advanced control Interfaces

Design and Implementation of Smart Wheelchair with Advanced ...

引用

international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shina Mifta Shifa Towfiq Mahmud Mridul Sadman Sakib Rafat Md. Hasan Monjur Md. Rokibul Islam Md. Kamrul Hassan Department of Electrical and Electronics Engineering American International University-Bangladesh Dhaka Bangladesh Department of Electrical and Electronics Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

As wheelchair users demand support and assistance from others in order to travel, the study intends to assist physically impaired persons with their mobility, particularly those who have lost their capacity to use their legs due to paralysis, an accident, or old age, as well as those who have feeling issues. The proposed project would provide wheelchair users with location-based services using one-finger control, voice control, and GPS tracking. In comparison to the expensive imported wheelchairs now on the market, the technology will be more affordable and user-friendly. With the support of a user command, the intelligent wheelchair gadgets will be able to maneuver themselves. Users will exert less effort because to the effortless mobility. The project will primarily use an E-bike motor and motor driver, an Arduino, a GPS module, and a solar panel. The voice control signal and manual control utilizing a joystick are used by the motor driver to operate. The Arduino includes a voice-activated device for physically challenged persons who are unable to control their hand motions. Family members will be able to trace the precise position in the case of emergencies due to the usage of GPS. The system will use a solar panel in addition to it to charge the battery. As an additional security measure, an emergency power OFF (EPO) switch is included. This switch is intended to turn the system off in an emergency or when it cannot be switched off normally. Using Porteous, the project simulation is carried out.

关键词： Transmitters Wheelchairs Prototypes Switches Signal processing Solar panels Security

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy

A Study on Coherence Length and Interferometry in Digital Ho...

引用

international conference on control, automation and Systems ( ICCAS)

作者： Takahiro Koga Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do Republic of Korea

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object.

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GPU Implementation of the Q-VLPL3D Plasma Simulation Code

GPU Implementation of the Q-VLPL3D Plasma Simulation Code

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informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Alexey V. Snytnikov Alexander M. Pukhov Mikhail M. Lavrentiev Institute of Automation and Electrometry SB RAS Novosibirsk Russian Federation Theoretical physics I University of Duesseldorf Duesseldorf Germany

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

Optimization of computer code for plasma beam simulation to achieve better performance is discussed. By using the advantages of the modern GPU, valuable results were achieved. Q-VLPL3D code uses quasi-static PIC method for simulation of plasma motion and the classic PIC for simulation of beam passing through plasma. The code is widely used to simulate plasma wakefield acceleration. Plasma wakefield acceleration is very interesting for both theoretical and experimental studies due to the possibility of building a compact and cheap charged particle accelerators. The Q-VLPL3D code was rewritten in a way more suitable for GPU, compared to the basic CPU version. It uses surface memory and implementation of 2D real-to real Fourier transform as GPU kernel. The performance of the GPU implementation was tested with recent Volta and Ampere GPUs resulting in the following performance increase: Volta performs particle push 120 times faster compared to Intel(R) Xeon(R) CPU E5-2698 v4 processor.

关键词： Particle beams Plasma simulation Codes Runtime Graphics processing units Performance gain Linear particle accelerator Plasmas Surface treatment Optimization

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Md Anikur Rahaman Maksura Binte Rabbani Nuha Md Tahsin Al Hossain Raihan Ul Islam Mohammad Rifat Ahmmad Rashid Ahmed Wasif Reza Shamim H Ripon Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model’s generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model’s strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies.

关键词： Deep learning Adaptation models Analytical models Accuracy Computational modeling Supervised learning Robustness Cervical cancer Medical diagnostic imaging Robots

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