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International CSI computer Conference, CSICC

作者： Mahmood Deypir Faculty of Computer Engineering Shahid Sattari University of Aeronautical Science and Technology Tehran Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Detecting vulnerable smart contracts has a direct effect on blockchain security because it helps users avoid using these contracts. In this study, the problem of vulnerability risk for blockchain smart contracts is introduced. Moreover, an effective criterion for its estimation is devised. With this criterion, to estimate the risk of an unknown smart contract, linear discriminant analysis of smart contracts and distances to their nearest neighbors were exploited. Although deep learning is not used in the proposed criterion and it requires little training data, it provides a realistic risk estimation of smart contracts. The experiments conducted on a real-world dataset of Ethereum blockchain smart contracts, including both vulnerable and safe contracts, show the acceptable performance of the proposed criterion. Moreover, the performance of the proposed criterion is superior to that of existing criteria in other areas of risk estimation.

关键词： Deep learning Smart contracts Estimation Training data Blockchains Linear discriminant analysis Stakeholders Risk management computer security

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A Hybrid Fault Diagnosis Method Based on Transformer and CNN 7

A Hybrid Fault Diagnosis Method Based on Transformer and CNN

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Gao, Zhi He, Meixuan Zhang, Bangcheng Shao, Yubo Liu, Jingru Que, Shaohua Changchun University of Technology School of Mechatronic Engineering Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China Changchun University of Technology Faculty of Mechanical and Electrical Engineering Faculty of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China Faw Tooling Die Manufacturing Changchun China

ISBN: (纸本)9798350363173

The elevator data of the welding production line has significant timing characteristics. There are more normal data and less abnormal data. Therefore, it becomes more complicated to diagnose the abnormality of the welding production line hoisting system through data analysis. This paper proposes a hybrid fault diagnosis method that combines a Transformer with a convolutional neural network. Firstly, the Transformer is used to extract the long-range dependencies in the data and identify complex faults that may not be adjacent. Then, the convolutional neural network extracted local or spatial features from the data. Finally, the multilayer perceptron maps the extracted features to the output space. The model innovatively merges the Transformer's global context analysis with the convolutional neural network's local feature detection precision, boosting fault diagnosis accuracy and efficiency. The proposed hybrid model was applied to the actual vibration data set of the hoists condition monitoring system of the white welding production line, and the results were contrasted with those of the support vector machine method, the long short-term memory network method, and the back propagation neural network method. Experimental findings demonstrate that the hybrid model achieves an accuracy of 99.98%, which is suitable for the fault diagnosis of the driving end bearing of the body of the white welding production line. © 2024 IEEE.

关键词： Failure analysis

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A non-Restraining Sheep Activity Detection and Surveillance using Deep Machine Learning 16

A non-Restraining Sheep Activity Detection and Surveillance ...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Ayub, Muhammad Yaseen Hussain, Abir Hassan, Muhammad Furqan Ul Khan, Bilal Khan, Farman Ali Al-Jumeily, Dhiya Khan, Wasiq Comsats University Islamabad Department of Computer Science Attock Campus Pakistan Sharjah University College of Engineering United Arab Emirates California State University School of Computer Science & Eng San BernardinoCA United States Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom

ISBN: (纸本)9798350381344

The number of livestock farms and their sizes (particularly the sheep farms) are on the rise, in response to the growing demands of food supply chain for increasing population. The detection and monitoring of sheep activities particularly in huge farms is tedious and challenging task. Therefore, a reliable and cost-effective sheep activity detection system which can be utilized for the virtual fencing, is of high demand. Existing data-driven approaches use accelerometer data for sheep monitoring and activity detection however, there are several limitations with these methods such as generating high volume of data with noise, relatively expensive, and not very reliable. This study presents a non-invasive computer vision-based approach along with deep transfer learning for sheep detection and determining whether the corresponding state is 'active' or 'inactive'. We complied a primary dataset comprising sheep in diverse poses and activities in a realistic outdoor environment. A custom YOLOV5s model is trained over new dataset and validated on purely unseen sheep instances for the model evaluation. The statistical outcomes demonstrate the robustness of proposed approach for various sheep activity detection. Our method has diverse implications and uses in the development of reliable and economical systems for monitoring sheep, particularly in extensive farms and virtual fencing applications. © 2023 IEEE.

关键词： Deep learning

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Virtual Digital Electronics engineering Laboratory for Online learning after COVID19

Virtual Digital Electronics Engineering Laboratory for Onlin...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Asifiqbal Thakor Shailesh Khant Computer Engineering Charusat Changa Anand India Faculty of Computer Science and Applications (FCA) Charusat Changa Anand India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Nowadays learning aspects are changed after Covid19. Parents are attracted towards new trends of Online education. An attempt is made here for crating virtual laboratory in engineering education. This paper is intended to create awareness among under graduate and post graduate students of technical courses about virtual laboratories The most basic subject which has to be learn by computer science, Information Technology, electrical and electronics engineering students is Digital Electronics. Virtual experiments are created here which do not require any kind of real hardware. An online tool Tinkercad is used here to simulate the basic experiments of Digital Electronics Circuits. Some of the basic experiments like logic gates, Boolean functions implementation, combinational circuits and sequential circuits are simulated here. This virtual laboratory allows students to understand and grasp the experiments by their own at flexible time and at own speed. Even Faculties can develop the basic projects and allow students to perform it.

关键词： COVID-19 Logic gates Market research Reliability engineering Hardware Sequential circuits Integrated circuit reliability

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Additive Decomposition of Bent Functions

Additive Decomposition of Bent Functions

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International Symposium on Multiple-Valued Logic

作者： Claudio Moraga Radomir S. Stanković Milena Stanković Faculty of Computer Science Technical University of Dortmund Germany Mathematical Institute S.A.S.A. Belgrade Serbia Dept. Computer Science Faculty of Electronic Engineering Niš Serbia

ISBN: (数字)9798331507442

ISBN: (纸本)9798331507459

An additive decomposition of multiple-valued bent functions is studied. It is shown that multiple-valued bent functions may be decomposed with respect to the arithmetic addition, the modulo p addition and the tensor sum of value vectors of p-valued functions. The number of some possible decompositions of bent functions is determined. Examples of additive decomposition of p-valued bent functions for p ∈ {3, 4, 5} are given. It is shown that under structural conditions there are functions which are not bent, but may have close related decompositions.

关键词： Additives Tensors Vectors Logic Arithmetic

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AI Cardiologist: Arrhythmia Detection by Transformer-Based Language Model 16th

AI Cardiologist: Arrhythmia Detection by Transformer-Based ...

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16th International Conference on ICT Innovations, ICT Innovations 2024

作者： Gusev, Marjan Faculty of Computer Science and Engineering Sts Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031861611

Wearable electrocardiogram sensors have emerged on the market but lack the technology to interpret the results with high performance. This paper explores the potential for innovation in developing a heart-based language and applying natural language processing to detect and classify dangerous arrhythmia. In the AI Cardiologist project, we propose real-time heart monitoring and alerting dangerous arrhythmia using AI-based technology using RoBERTa transformers in large language models. The focus is on analyzing the domains of medical applications and introducing new business models to exploit the technology’s results. We confirmed that foundation models for ECG records created by deep learning methods could greatly enhance precision and effectiveness of heart health monitoring and detection of atrial fibrillation, enabling better healthcare and well-being. No similar solution is available on the market outside of hospital environments, giving the project results a high potential for commercialization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Efficient multimodal cancelable biometric system based on steganography and cryptography

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Iran Journal of computer science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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An Accelerated Coordinate Descent Method for Support Vector Machine 8

An Accelerated Coordinate Descent Method for Support Vector ...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Kamandi, Ahmad Alyannezhadi, Mohammad M. Amiri, Zahra University of Science and Technology of Mazandaran Department of Mathematics Behshahr Iran University of Science and Technology of Mazandaran Department of Computer Science Behshahr Iran Shahrood University of Technology Faculty of Computer Engineering Shahrood Iran

ISBN: (纸本)9781665476232

Support vector machines (SVMs) are famous and important classifiers in classification researches. SVM and its application have been used in an enormous amount of research in various scientific domains in recent years. There are different methods to solve SVM problems. Coordinate descent methods are one of the main categories of methods for solving these problems. In this paper, using the generalization of Aitken's Δ 2 process to the vector case, a new accelerated coordinate descent algorithm is proposed. The new algorithm is tested with three different datasets. The numerical results indicate the efficiency of the new algorithm in the concept of increasing the speed of convergence. © 2022 IEEE.

关键词： Support vector machines

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Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis

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Materials Today: Proceedings 2023年 80卷 3546-3553页

作者： Shrivastava, Anurag Nayak, Chinmaya Kumar Dilip, R. Samal, Soumya Ranjan Rout, Sandeep Ashfaque, Shaikh Mohd ECE Lakshmi Narain College of Technology and Science Indore India Faculty of Emerging Technology Sri Sri University Odisha India Dept. of Mechatronics Eng. Acharya Institute of Technology India Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria Faculty of Agriculture Sri Sri University Cuttack India Department of Computer Engineering Rizvi College of Engineering India

In this digital world, all the developing countries' growth has improved drastically with farmers' influence and innovative farming processes. Generally, the farming process includes ancient traditional methodologies for maintaining the crops' quality and yields. Their farming was developed and has given more profit only with the quality of the soil and the nutrition used on land. But the drawback is they were spending much time to get their yields from their land, and the nutrition level was not maintained at all times. Moreover, more space was used for farming, with huge manpower required to maintain the entire land. Most countries are moved to smart farming concepts with IoT platforms to optimize the time and techniques. In that hydroponic, the best innovative idea to produce more crops, vegetables, and fruits without soil. Rockwool is used for farming processes with water contaminants at regular intervals will provide huge productions as well as no need to wait for a long time for cultivation. This method was implemented in most of the countries that were doing smart farming with less manpower and low cost. The hydroponic farming methodology is implemented with IoT sensors for monitoring crop's status and health continuously. Once their nutrition level or water level has decreased, it will provide all at constant time intervals to the entire system effectively. A few years ago, hydroponic farming was horizontally implemented on smaller spaces for the regular water flow. But now a day it is implemented on a vertical surface to reduce the space, and water flow is only at the time of need. This technology is used to increase the productivity of the crops with a small space of land and less manpower. Perhaps the cost of the entire system has been taken into consideration by small-scale unit farmers;vertical hydro farming provides better results when compared with previous classical methods. This research paper has given the design and implementation of automated ver

关键词： Hydroponics

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Extracting Entities and Relations in Analyst Stock Ratings News 15th

Extracting Entities and Relations in Analyst Stock Ratings...

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15th International Conference on ICT Innovations, ICT Innovations 2023

作者： Krstev, Ivan Mishkovski, Igor Mirchev, Miroslav Golubova, Blagica Gramatikov, Sasho Faculty of Computer Science and Engineering Rugjer Boshkovikj 16 Skopje1000 Macedonia

ISBN: (纸本)9783031543203

Massive volumes of finance-related data are created on the Internet daily, whether on question-answering forums, news articles, or stocks analysis sites. This data can be critical in the decision-making process for targeting investments in the stock market. Our research paper aims to extract information from such sources in order to utilize the volumes of data, which is impossible to process manually. In particular, analysts’ ratings on the stocks of well-known companies are considered data of interest. Two subdomains of Information Extraction will be performed on the analysts’ ratings, Named Entity Recognition and Relation Extraction. The former is a technique for extracting entities from a raw text, giving us insights into phrases that have a special meaning in the domain of interest. However, apart from the actual positions and labels of those phrases, it lacks the ability to explain the mutual relations between them, bringing up the necessity of the latter model, which explains the semantic relationships between entities and enriches the amount of information we can extract when stacked on top of the Named Entity Recognition model. This study is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relations between them. Furthermore, the multilingual abilities of a joint pipeline are being explored by combining English and German corpora. For both subtasks, we record state-of-the-art performances of 97.69% F1 score for named entity recognition and 89.70% F1 score for relation extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Investments

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