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Xception CNN-Ensemble Learning Based Facial Emotion Recognition

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

引用

International Conference on Computing Communication control and Automation (ICCUBEA)

作者： Supriya Shirsath Vaishali Vikhe Pratap Vikhe Dept. of Computer Engineering Pravara Rural Engineering College Ahmednagar India Dept. of Instrumentation & Control Engineering Pravara Rural Engineering College Ahmednagar India

ISBN: (纸本)9781665484527

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset.

关键词： Training Emotion recognition Computational modeling Face recognition Anxiety disorders Neural networks Machine learning

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications

Velocity-aided Channel Estimation for Spatially Selective mm...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.

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Underwater and In-Air IPT-CPT Wireless Power Transfer Performance Comparison: A Simulation Study

Underwater and In-Air IPT-CPT Wireless Power Transfer Perfor...

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International Conference on Environment and Electrical engineering (EEEIC)

作者： Sadjad Shafiei Seyed Saeid Heidari Yazdi Mostafa Kermani Almaz Saukhimov Arsalan Hekmati Mehdi Bagheri dept. of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan dept. of Electrical Engineering Sapienza University of Rome Rome Italy dept. of Electrical Systems and Networks Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan Revterra Company Houston Texas USA

In this study, an inductive-capacitive coupling wireless power transmission (IC-UWPT) system in the seawater environment is discussed. It is revealed that the conductive characteristic of seawater medium will change the eddy current flow as compared to the air medium, and in higher frequency ranges, will increase power dissipation and affect the total efficiency and ultimately the system's performance. A new equivalent circuit is provided to precisely model the seawater WPT, where the eddy current losses are referred to as an additional term coil's parameter in the circuit model. Using Ansys Electronics, simulation studies are carried out in both air and water environment. The eddy current and electrostatic solutions are adopted for IPT and CPT system analysis. Moreover, Ansys Simplorer is utilized to couple the proposed IPT-CPT combined system. The simulation results validate the introduced model for the underwater IPT -CPT system and its performance.

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Unveiling the Unseen: Leveraging Zero-Day Attack Detection Using Unsupervised and Semi-Supervised Learning

Unveiling the Unseen: Leveraging Zero-Day Attack Detection U...

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International Conference on computer Theory and Applications (ICCTA)

作者： Osama A. El Awadia Sameh A. Salem NTRA Research and Technical Solutions Unit Egyptian Computer Emergency Readiness Team (EG-CERT) Egypt Computer & Systems Engineering Dept. Faculty of Engineering Helwan University Cairo Egypt

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

In the ever-evolving cybersecurity landscape, detecting unseen, zero-day attacks is both urgent and paramount. These sophisticated attacks often lack precedent, posing a challenge to conventional machine learning techniques that rely on prior knowledge and training data. This paper endeavors to detect zero-day and unseen cyber attacks using zero-shot machine learning technique, which holds the promise of identifying these attacks without any prior exposure. This work explores the effectiveness of unsupervised learning in zero-day attack detection. The experimental results demonstrate that autoencoders can identify anomalies in data, which are typically associated with zero-day attacks. When compared with other unsupervised and semi-supervised learning methods, the proposed autoencoder algorithm outperforms its competitors and achieves an accuracy of 99.9%, shedding light on its relative effectiveness in zero-day attack detection.

关键词： Accuracy computer hacking Autoencoders Zero shot learning Training data Intrusion detection Machine learning Semisupervised learning Unsupervised learning Cyberattack

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Comparative Evaluation of Machine Learning-Based controller Area Network Intrusion Detection systems

Comparative Evaluation of Machine Learning-Based Controller ...

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International Conference on Information and Communication systems (ICICS)

作者： Omar Y. Al-Jarrah Omar A. Ramadan Baha Alsaify Suboh Alkhushayni Dept. Network Engineering and Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Dept. Mechanical Engineering Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Dept. Information Systems Yarmouk University Irbid Jordan

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN.

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Price Optimisation and Management 3

Price Optimisation and Management

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shafkhan, M.T.M Jayasundara, P.R.S.S Kariyapperuma, K.A.D.R.L Lakruwan, H.P.S Rupasinghe, Lakmal Sri Lanka Institute of Information Technology Dept. of Computer and Software Eng Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income;everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded;some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-To-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this. © 2021 IEEE.

关键词： Cost benefit analysis

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Coordinate Attention-enabled Ship Object Detection with Electro-optical Image 22

Coordinate Attention-enabled Ship Object Detection with Elec...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Hongbin Xu Xiantao Jiang Tao Yin Qi Cen Zhijian Zhang Tian Song F.Richard Yu Dept.Information Engineering Shanghai Maritime University China Ningbo Special Equipment Inpection and Research Institute China Dept. Electrical and Electronics Engineering Tokushima University Japan Dept. Systems and Computer Engineering Carleton University Canada

ISBN: (纸本)9781450397056

Shipping safety is one of the factors restricting the development of navigation. In particular, the route near the shore is prone to unknown risks due to the existence of multiple types of ships, the density of ships, the shielding between ships, and other reasons. This paper presents a method for detecting medium-range ships, which can improve security for ships. This method is based on the You Only Look Once Version 5 network (YOLOv5). To improve the accuracy, the coordinate attention model is integrated into the detection network. The main research content and experimental work of this paper are as follows. Firstly, the YOLOv5 network and spatial attention mechanism are analyzed. Then, detection experiments were carried out based on YOLOv5 and Singapore Maritime Data Set (SMD). Then, the coordinate attention model was used to improve the network. Finally, by adjusting training parameters and improving attention, the mAP of test results of the object detection network reaches 73%, and the feasibility of object detection of the YOLOv5 algorithm with coordinate attention is confirmed.

关键词： Ship Object Detection Coordinate Attention Mechanism You Only Look Once Version 5 (YOLOv5)

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Mathematical Model of Damping of UAV Oscillations in the Cargo Delivery Problem

Mathematical Model of Damping of UAV Oscillations in the Car...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Dmytro Kucherov Tetiana Shmelova Olexii Poshyvailo Valerii Tkachenko Ignat Miroshnichenko Ihor Ogirko dept. Computerized Control Systems Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine dept. Air Navigation Systems Faculty of Air Navigation Electronics and Telecommunications National Aviation University Kyiv Ukraine dept. Mathematic Ukrainian Academy of Printing City Lviv Ukraine

Currently, the use of unmanned vehicles for the delivery of goods is becoming a trend in many areas of human activity. At the same time, it is necessary to solve the problem of the integrity of the cargo because of its possible fluctuations. The paper deals with the problem of delivering cargo attached to a 4-motor UAV on a rigid cable. A control system is needed to achieve the UAV equilibrium position that can compensate for significant mismatches in the XY coordinate plane. A combination of two control algorithms is proposed for solving this problem, one of which provides a given position in the horizontal plane at small mismatch angles and the second at large mismatch angles. The first controller uses the PID controller algorithm with the corresponding optimal setting of its parameters, and the second one uses the bang-bang control scheme. An additional controller is used to coordinate these controllers, which ensures their timely switching. Some simulation is also proposed.

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Fuzzy Logic controller for the Chemotherapy of Brain Tumor

Fuzzy Logic Controller for the Chemotherapy of Brain Tumor

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Muhammad Zubair Daniela Iacoviello Iqra Shafeeq Mughal Dept. Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. di Ingegneria Electrica Electronica e Informatica DIEEI University of Catania Catania Italy

An advanced Fuzzy Logic controller (FLC) that considers all the states of the brain tumor system is designed for the chemotherapy treatment. A Mamdani-type FLC is proposed for dynamically controlling the chemotherapy drug for the tumor system; the chemotherapy treatment of brain tumors requires advanced strategies which mainly depend upon the severity of the tumor. In this work, the advanced FLC designed aims both at determining the amount of chemotherapy to eliminate tumor cells, and at preserving the minimum amount of healthy and immune cells. The controller's performance is verified using MATLAB software based on different control parameters, showing its effectiveness in reducing the tumor cells. It has shown favorable results in terms of steady-state error, rate of convergence, and amount of drug consumed.

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Defect Detection for Printed Circuit Board Assembly Using Deep Learning 8

Defect Detection for Printed Circuit Board Assembly Using De...

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8th International Conference on control Science and systems engineering, ICCSSE 2022

作者： Ren, Jing Gabbar, Hossam A. Huang, Xishi Saberironaghi, Alireza Ontario Tech University Dept. of Electrical and Computer Engineering OshawaON Canada Faculty of Energy Systems and Nuclear Science Engineering and Applied Science Ontario Tech University OshawaON Canada Rs Opto Tech Ltd Jiangsu Suzhou China

ISBN: (纸本)9781665484138

In many industrial applications, the number of defect samples is often insufficient for defect detection using conventional deep learning techniques. Also, the frequent change of PCBA board types on the product line introduces new defect types and adds a layer of challenge to the detection task considered in this paper. We propose a deep learning algorithm that targets learning patterns from various defect types with unbalanced training samples in the PCBA manufacturing product lines. A novel batch sampling method is proposed for the deep learning method for PCBA defect detection. We have validated the proposed algorithm using normal and defective images. The results show that the proposed deep learning method can accurately identify defects in PBCA images and achieve an overall accuracy of 98%. This deep learning technique can also be extended to detect other surface-level defects. © 2022 IEEE.

关键词： Defects

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