检索结果-内蒙古大学图书馆

8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Sulistyohati, Aprilia Susanti, Lia Ridwan, Ridwan Paramita, Aulia Nastiti, Tashia Indah Indraprasta PGRI University Informatics Engineering Faculty of Engineering and Computer Science South Jakarta Indonesia

ISBN: (纸本)9781665453899

Universities have a difficult task in preparing their students to have competencies according to the needs of the digital era industry. This study aims to determine the effect of hard skills, soft skills, and digital literacy on IT student work readiness. This is quantitative research, and the population is students of informatics engineering at Indraprasta University in semester 7 or 8 who have not worked. The number of samples is as many as 125 students (the minimum sample from the calculation of Slovin with an error rate of 5% is 93). Questionnaires were distributed through a simple random sampling technique. The data were processed using multiple linear regression analysis techniques. The results of this study obtained the equation Y = 1.821 + 0.757X1 +0.380X2 + 0.306X3 + e. Simultaneously hard skills, soft skills, and digital literacy have a positive and significant 94.3% effect on IT work readiness. Partially, the effect of hard skills (X1), soft skills (X2), and digital literacy (X3) on IT student work readiness (Y) is 37.4%, 34.3%, and 23.6%. This study proves that hard skills (X1), soft skills (X2), and digital literacy (X3) competencies together have a significant effect on IT student work readiness (Y) by 94.3%. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization 1

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Nüßlein, Jonas Zielinski, Sebastian Gabor, Thomas Linnhoff-Popien, Claudia Feld, Sebastian Institute for Informatics LMU Munich Munich Germany Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

We introduce a novel approach to translate arbitrary 3-sat instances to Quadratic Unconstrained Binary Optimization (qubo) as they are used by quantum annealing (QA) or the quantum approximate optimization algorithm (QAOA). Our approach requires fewer couplings and fewer physical qubits than the current state-of-the-art, which results in higher solution quality. We verified the practical applicability of the approach by testing it on a D-Wave quantum annealer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kusnik, Damian Smolka, Bogdan Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350322972

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios. © 2023 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Adaptive Technique of Impulsive Noise Removal in Color Digital Images 10

Adaptive Technique of Impulsive Noise Removal in Color Digit...

引用

10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Smolka, Bogdan Mendrela, Monika Kusnik, Damian Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350304299

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Automobile Spare Part Classification via AI Image Processing Techniques, a Comparison Study 12

Automobile Spare Part Classification via AI Image Processing...

引用

12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Lee, Boon Hoe Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Majeed, Anwar Abdul P.P. University of Aleppo Faculty of Informatics Engineering Syria Sunway University Bandar Sunway School of Engineering and Technology Selangor Darul Ehsan47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350391398

Artificial intelligence (AI) and image recognition technologies are developing rapidly, creating new opportunities for process improvement in industry, especially in spare parts classification. Spare parts classification using images has not been thoroughly researched and utilized in the car repair industry, especially in Malaysia. This project seeks to create a reliable and effective system that uses AI to identify and classify spare parts from user-uploaded images precisely. The main goals are to simplify inventory management and improve the precision and speed of part identification in diverse industrial environments. In the end, an accuracy of 98.70% was achieved for spare part type classification and 89.70% for both type and car brand classification with the use of transfer learning, finetuning, and object detection techniques. © 2024 IEEE.

关键词： Inventory control

来源：评论

学校读者我要写书评

暂无评论

Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

引用

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization 6

A Framework for Democratizing Open-Source Decision-Making us...

引用

6th International Conference on Computing and informatics, ICCI 2024

作者： Afifi, Khalifa H. Fouad, Khaled M. Asian, Heba K. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computers and Engineering New Mansoura University Egypt Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairo Egypt

ISBN: (纸本)9798350373875

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general. © 2024 IEEE.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Classification of Alzheimer’s Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer Science 2023年第3期50卷

作者： Oktavian, Muhammad Wildan Yudistira, Novanto Ridok, Achmad Informatics Engineering Faculty of Computer Science Brawijaya University 65145 Indonesia Informatics Engineering Faculty of Computer Science Brawijaya University 65145 Indonesia

Alzheimer’s disease is a progressive neurodegenerative disorder that gradually deprives the patient of cognitive function and can end in death. With the advancement of technology today, it is possible to detect Alzheimer’s disease through Magnetic Resonance Imaging (MRI) scans. With MRI technology and image recognition, early diagnosis of Alzheimer’s disease can be performed automatically using machine learning. Although machine learning has many advantages, currently, deep learning is more widely applied because it has more robust learning capabilities and is suitable for solving image recognition problems. However, several challenges must be faced to implement deep learning, such as the need for large datasets, computing resources, and careful parameter setting to prevent overfitting or underfitting. In responding to the challenge of classifying Alzheimer’s disease using deep learning, this study proposes the Convolutional Neural Network (CNN) with the Residual Layer (ResNet-18) architecture. Transfer learning from ImageNet and weighting the loss function are then applied so that each class has a weight depending on its size. It is proposed to overcome the need for a large and balanced dataset. Furthermore, this study experimented with changing the network activation function to a Mish activation function. From the results of the tests, the model’s accuracy of 88.30% is produced using transfer learning, weighted loss, and the Mish activation function. This accuracy is better than the baseline model, which only gets an accuracy of 69.10%. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

引用

2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：