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54th CIRP Conference on Manufacturing Ssystems, CMS 2021

作者： Schäfer, Tristan Bergmann, Jim A. Carballo, Rafael Garcia Rehof, Jakob Wiederkehr, Petra Software Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Straße 12 Dortmund44227 Germany Virtual Machining Department of Computer Science TU Dortmund University Otto-Hahn-Straße 12 Dortmund44227 Germany Faculty of Mechanical Engineering TU Dortmund University Leonhard-Euler-Straße 5 Dortmund44227 Germany

The planning of machining operations for components using CAM requires careful consideration by highly trained personnel, with rising complexity in part requirements. This work proposes a novel domain-specific planning technique using Combinatory Logic Synthesis to generate a multitude of tool paths. By analyzing current state CNC path planning algorithms, motion primitives are identified and implemented in a modular representation using software components. Thus, a large variety of path planning strategies can be generated, while maintaining a multitude of different boundary constraints and productivity metrics. These solutions are subjected to geometric physically-based simulations in order to validate their ranking and compliance. © 2021 The Author(s).

关键词： Motion planning

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Nikhilesh Pramod Mankar Tushar Mahure Anushka A. Joshi Sagar Dhanraj Pande Aditya Khamparia Ankita Shrikrushna Nathe Fadi Al Turjman School of Engineering and Technology Pimpri Chinchwad University (PCU) Pune India Independent Researcher Amravati India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India Department of Information Technology Kavikulguru Institute of Technology and Science Nagpur India Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI

关键词： Cyberbullying Entertainment industry Machine learning Gain measurement Natural language processing Internet Bayes methods computer crime

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A Novel Trust Assessment System for Online Social Networking Environment Using Learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Nithya D. Deepa Vunnava Dinesh Babu Pallavi G Hamed Fawareh R. D. Kayalvizhy Department of Computer Science Engineering Saveetha Engineering College Chennai Department of CSE St. Joseph's College of Engineering Chennai Department of CSE RV Institute of Technology Guntur Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks.

关键词： Measurement Machine learning algorithms Social networking (online) Privacy breach Real-time systems Hazards Maintenance Internet Fake news Trust management

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In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking

arXiv

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arXiv 2024年

作者： Gao, Peng Li, Shi-Min Gao, Feng Wang, Fei Yuan, Ru-Yue Fujita, Hamido School of Cyber Science and Engineering Qufu Normal University Shandong Qufu273165 China School of Computer Science and Technology East China Normal University Shanghai200062 China School of Integrated Circuits Harbin Institute of Technology Shenzhen Guangdong518055 China Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Faculty of Software and Information Science Iwate Prefectural University Iwate Takizawa020-0611 Japan

Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking. However, relying solely on deep learning models to obtain better tracking results requires carefully selecting feature information that is beneficial to representing the target object and designing a reasonable template update strategy, which undoubtedly increases the difficulty of model design. Thus, recent TIR tracking methods face many challenges in complex scenarios. This paper introduces a novel Deep Bayesian Filtering (DBF) method to enhance TIR tracking in these challenging situations. DBF is distinctive in its dual-model structure: the system and observation models. The system model leverages motion data to estimate the potential positions of the target object based on two-dimensional Brownian motion, thus generating a prior probability. Following this, the observation model comes into play upon capturing the TIR image. It serves as a classifier and employs infrared information to ascertain the likelihood of these estimated positions, creating a likelihood probability. According to the guidance of the two models, the position of the target object can be determined, and the template can be dynamically updated. Experimental analysis across several benchmark datasets reveals that DBF achieves competitive performance, surpassing most existing TIR tracking methods in complex scenarios. © 2024, CC BY-NC-SA.

关键词： Information filtering

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COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

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Soft computing 2023年第5期27卷 2657-2672页

作者： Alaa S Al-Waisy Shumoos Al-Fahdawi Mazin Abed Mohammed Karrar Hameed Abdulkareem Salama A Mostafa Mashael S Maashi Muhammad Arif Begonya Garcia-Zapirain Communications Engineering Techniques Department Information Technology Collage Imam Ja'afar Al-Sadiq University Baghdad Iraq. Computer Science Department Al-Ma'aref University College Anbar Iraq. College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq. College of Agriculture Al-Muthanna University Samawah 66001 Iraq. Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia. Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 11451 Saudi Arabia. School of Computer Science Guangzhou University Guangzhou China. eVIDA Lab The University of Deusto Avda/Universidades 24 48007 Bilbao Spain.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

关键词： Chest X-ray images Chest radiography imaging Coronavirus COVID-19 epidemic Deep learning ResNet34 model Transfer learning

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Traceability and Identity Protection in Smart Agricultural IoT System Framework Based on Blockchains

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Xie, Mingyue Liu, Jun Chen, Shuyu Lin, Mingwei Xu, Guangxia Xu, Zeshui Chang, Zheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Chongqing University School of Big Data and Software Engineering Chongqing400065 China Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Sichuan University Business School Chengdu610000 China University of Jyväskylä Faculty of Information Technology P.O. Box 35 JyväskyläFIN-40014 Finland

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented. © 2004-2012 IEEE.

关键词： Crop protection

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NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

arXiv

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural Language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

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A computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

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Mobile Information Systems 2023年第1期2023卷

作者： Zahid, Hira Syed, Sidra Abid Rashid, Munaf Hussain, Samreen Umer, Asif Waheed, Abdul Nasim, Shahzad Zareei, Mahdi Mansoor, Nafees Biomedical Engineering Department and Electrical Engineering Department Ziauddin University Karachi74600 Pakistan Biomedical Engineering Department Sir Syed University of Engineering and Technology Karachi75300 Pakistan Software Engineering Department Sir Syed University of Engineering and Technology Karachi74600 Pakistan Electronic Engineering Department Dawood University of Engineering & Technology Karachi74800 Pakistan Department of Computer Science & IT Hazara University Mansehra23100 Pakistan Department of Computer Science Women University Swabi Swabi23430 Pakistan School of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Begum Nusrat Bhutto Women University Faculty of Management Information Science and Technology Sukkur Pakistan Tecnologico de Monterrey School of Engineering and Sciences Monterrey64849 Mexico Dhaka Bangladesh

Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model. © 2023 Hira Zahid et al.

关键词： Image recognition

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Application of soft computing algorithms for hybrid modular multilevel inverters

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Measurement: Sensors 2024年 31卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Bhoopal, Neerudi Kumar Gatla, Ranjith Kotb, Hossam AboRas, Kareem M. Elrashidi, Ali Alqarni, Mohammed Yasin Ghadi, Yazeed Oubelaid, Adel Department of Electrical & Electronics Engineering B V Raju Institute of Technology Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Electrical Engineering Department University of Business and Technology Ar Rawdah Jeddah23435 Saudi Arabia Engineering Mathematics Department Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Laboratoire de Technologie Industrielle et de L'Information Faculté de Technologie Université de Bejaia Bejaia06000 Algeria

This study utilized Harris Hawk optimization to analyze the harmonic distortions of a symmetric capacitor based multilevel converter. The proposed multilevel converter uses identical and symmetric DC sources in its input. The proposed topology for a multilevel converter uses fewer switches than typical cascaded designs. It can output 9-level outputs and can be scaled up to higher levels. The proposed multilevel converter can be improved by implementing a low frequency pulse width modulation technique to reduce the stress on the switches. The optimization of the switching angles using Harris Hawk was able to solve the non-linear aspects of the Selective Harmonic Elimination Pulse Width Modulation. The output voltage's THD was compared with that of other optimization methods, such as ant colony and particle swarm. The comparison shows that the output THD of the Harris Hawk optimizer is lower than that of other methods, which is about 5 %, according to the standards of IEEE-519. © 2023

关键词： Pulse width modulation

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