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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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A Comprehensive Analysis of Protein Secondary Structure Prediction Models 3

A Comprehensive Analysis of Protein Secondary Structure Pred...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Shukla, Akanksha Devarajan, Ganesh Gopal Agarwal, Shivani Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Delhi NCR Campus Delhi Meerut Road Modinagar Uttar Pradesh Ghaziabad201204 India Department of Information and Technology Akgec Ghaziabad India

ISBN: (纸本)9798331519582

Proteins are vital for numerous biological functions, and understanding their secondary structure is essential for unravelling their function. Predicting secondary protein's structure has been the focus of extensive study throughout the years. This article brings forth an rigorously overview of the latest advanced methods and approaches used for protein secondary structure prediction also aims to come up with researchers and practitioners in the field with a valuable resource for advancing the creation of dependable and precise contraption for anticipating protein secondary structures. This paper presents a benchmarking study of different secondary structure prediction methods, evaluating their accuracy, strengths, and limitations. We discuss widely used techniques such as the Chou-Fasman method, GOR method, PHD method, and more recent approaches based on machine learning and deep learning. This detailed summary is designed to assist researchers in determining the most suitable method for their specific needs research requirements and further the field of protein structure prediction. © 2025 IEEE.

关键词： Deep learning

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Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario 13

Refined LSTM Network for Sensor-based Human Activity Recogni...

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13th IEEE International Conference on Software Engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Sensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognition is employed in surveillance systems, patient health monitoring, and many other systems involving the interaction between human and intelligent wearable devices, including smartphones and smartwatches. The primary objective of this study work is to identify human behavior in the actual world. We proposed an improved long short-term memory network called RLSTM that uses a squeeze-and-excitation module to efficiently identify human actions and enhance action identification systems' interpretation. A publicly available real-world dataset known as REALWORLD16 was used to train and validate the model five times to analyze the proposed network. The proposed RLSTM achieved the highest accuracy of 98.04% and F1-score of 97.76%, as determined by several investigations. © 2022 IEEE.

关键词： Wearable sensors

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Exploring Botnet Attacks with Rapid Miner: A Comprehensive Study

Exploring Botnet Attacks with Rapid Miner: A Comprehensive S...

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International Conference for Convergence of technology (I2CT)

作者： Salman Asghar Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Summaira Nosheen Ali Moiz Qureshi Adeel Ahmad Siddiqui Zaima Mubarak Saad Hussain Chuhan Muzzamil Mustafa Muhammad Atif Yaqub Afshan Bilal Department of Computer Science Bahria University Lahore Lahore Pakistan Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The fast growth of networks has been shown to have a clear causal relationship with the recent uptick in botnetbased attacks. The increasing use of botnets in various types of cyberattacks, such as denial of service (DoS), distributed denial of service (DDoS), backdoor attacks, and other types of cyberattacks, presents a significant challenge to the network security of organizations. By using the collected bandwidth and system resources of its victims, the botnet has the potential to cause damage that cannot be repaired to the network. The purpose of this paper is to conduct a comprehensive analysis of botnet attacks, with a concentration on DoS and DDoS attacks. It is easy to draw presumptions and construct views on bots based on their activities and traits. Models were trained via machine learning, specifically Auto Modeling, to help defend themselves against attacks launched by botnets. The trained model provides both tabular and visual representations of the outcomes of DoS and DDoS attacks. The model represents the results of DoS and DDoS attacks in tabular and graphical form. Random forest and generalized linear models were used because they showed the best performance on the dataset. Wireshark was used to ensure that the opening of the monitored network went off without a hitch. The pcap data extracted from the network was loaded in Rapid Miner for model *** paper analyzes the potential for DoS and DDoS attacks.

关键词： Visualization Botnet Organizations Network security Denial-of-service attack Chatbots computer crime

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An Omnidirectional Antenna with Dual-Gain and High-Gain for WiFi-6 Applications 6

An Omnidirectional Antenna with Dual-Gain and High-Gain for ...

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Wang, Haolin Feng, Botao Ding, Xiao Deng, Li Sun, Sanshan Gu, Wenzhe College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China College of Physics and Electronic Engineering Sichuan Normal University Sichuan China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (纸本)9798350399059

This letter proposes a high-gain omnidirectional antenna with dual-band for WiFi-5/WiFi -6 applications. The antenna is basically comprised of several slotted semi-circular radiation patches, L-shaped branch structure patches and coaxial feed sources. Here, symmetrical rectangular slots are used to increase impedance matching. The planar size of antenna is 12×35 mm2, which is much smaller than other dual-band WIFI antennas. The simulated results tell that the antenna can be used at 2.42.485 GHz and 5.155.85 GHz (|S11| © 2023 IEEE.

关键词： Dielectric materials

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Object Identification and Localization of Visual Explanation for Weapon Detection

Object Identification and Localization of Visual Explanation...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Yuenyong, Sumeth Wongpatikaseree, Konlakorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation. © 2022 IEEE.

关键词： Convolutional neural networks

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Enhancing Service Management in Industry 4.0: The Role of AI-Powered Chatbots

Enhancing Service Management in Industry 4.0: The Role of AI...

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Advances in science and Engineering technology International Conferences (ASET)

作者： Asraa Mohammed Albeshr Fady Alnajjar Hassan Abdulmouti Omar Mubin College of Information Technology UAE University Alain UAE Engineering Technology & Science Faculty of Engineering Higher College of Technology Sharjah UAE School of Computer Data and Mathmatical Science Westren Sydney University Sydney Australia

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

In the era of Industry 4.0, where digital transformation is crucial, developing advanced chatbot system becomes essential to revolutionize service management and bolstering business continuity. By leveraging Artificial Intelligence (AI) within such industrial frameworks, smartchatbots promise to streamline operations, ensure seamless customer interactions, and drive efficiency, thereby fortifying businesses in today's competitive landscape. This study presents an adaptable smart chatbot model designed to mimic and absorb the expertise of technical officers within a factory setting, responding to employee technical queries. This approach aims to alleviate the burden on technical personnel while enhancing response efficiency. Unlike traditional systems, this adaptive model benefits from continuous learning capabilities, enabling it to refine its responses over time based on new interactions and corrections made by technical officers. Furthermore, the chatbot's 24/7 availability and the collaborative feedback mechanism for evaluating its responses ensure that it not only meets but exceeds user expectations. Through detailed analysis and empirical evidence, we demonstrate how this chatbot significantly enhances work quality and operational continuity, setting a new benchmark for digital service management in the context of Industry 4.0. The results from conducting surveys among the technical officers regarding the chatbot's usefulness were overwhelmingly positive. All participants expressed high satisfaction, confirming that the implementation of such a smart chatbot model significantly reduces their load and enhances the user experience.

关键词： Surveys Adaptation models Chatbots User experience Production facilities Fourth Industrial Revolution Personnel Artificial intelligence Business Load modeling

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A Simple Directional Patch Antenna with High Gain for WLAN Applications 6

A Simple Directional Patch Antenna with High Gain for WLAN A...

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Zhang, Zhenfeng Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Zeng, Qingsheng College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350399059

This paper presents a high-gain directional antenna, which is suitable for 2.4 GHz wireless local area network (WLAN) applications. The antenna consists of a T-shaped patch etched with a rectangular slot, a rectangular ground plane, a reflector with metal strips around it. By adding symmetrical rectangular metal sheet around the antenna, the profile of the antenna can be reduced to 0.112 λ 0 (where λ 0 is the wavelength of 2.4 GHz in the free space), and the bandwidth can be expanded. This work is constructed using the finite element method based on the ANSYS HFSS software. The antenna has an impedance bandwidth of 35.4% (2.23-3.08 GHz), and the corresponding gain varies within 7.43 ± 0.02 dBi. The proposed antenna with the simple and thin structure is suitable for the applications such as bluetooth and zigbee. © 2023 IEEE.

关键词： Slot antennas

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on information technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Sentiment Analysis for Arabic Product Reviews Using LLMs and Knowledge Graphs

Sentiment Analysis for Arabic Product Reviews Using LLMs and...

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Computing and Informatics (ICCI), International Conference on

作者： Abdelrahman Lotfy Kirollos Saleh Saher Mohamed John Lorance Ehab Yehia Khaled Mohammed Ibrahim AbdAlbaky Mostafa Fathy Tawfik Yasser School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Faculty of Computer Science and Engineering Benha University New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

The exploration of sentiment analysis in multilingual contexts, particularly through the integration of deep learning techniques and knowledge graphs, represents a significant advance in language processing research. This study specifically concentrates on the Arabic language, addressing the challenges presented by its morphological complexity. While the primary focus is Arabic, the research also includes a comprehensive review of related work in other languages such as Bangla and Chinese. This contextualizes the challenges and solutions found in Arabic sentiment analysis within a broader multilingual landscape. Utilizing pre-trained language models like BERT, the research has achieved noteworthy improvements in sentiment analysis accuracy and efficiency, particularly for the Arabic language. The integration of knowledge graphs stands out as a crucial innovation, offering essential contextual insights and mitigating the limitations posed by sparse labeled datasets in Arabic, a language less resourced compared to English. The findings of this study highlight the effectiveness of tailored BERT models for Arabic sentiment analysis, revealing the vast potential and inherent challenges of employing knowledge graphs and large language models for a deeper, more nuanced understanding. The future direction of this research includes enhancing these methods with cutting-edge machine learning techniques, aiming to further refine sentiment analysis processes and knowledge graph construction with a focus on Arabic within a multilingual framework.

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