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检索条件"机构=Applied Computer Science I-Software Engineering"

共 2312 条记录，以下是221-230 订阅

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Comparison of Standard and Modified Ant Colony Algorithm with Fuzzy system for Path Planning of Robot in Complex Environment

Comparison of Standard and Modified Ant Colony Algorithm wit...

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international Conference on intelligent Systems: Theories and Applications (SiTA)

作者： Sudeep Sharan Anh Tong Ngoc Minh Peter Nauth Juan José Domínguez-Jiménez Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

The requirements of optimizing path planning for the autonomous robot are in high demand in such industrial communities, especially in manufacturing and caring social support. The application of meta-heuristic methods in autonomous problems is considered because of their adaptivity and robustness. Ant Colony Optimization (ACO) is one of the researchers’ approaches because of its effectiveness in the ant community. However, some constraints are making this method less productive. Therefore, this paper is to generate a modified ACO combined with Fuzzy logic (ACOFL) to minimize its drawbacks and maximize the robustness of path planning. This study presents the role of ACO algorithms in Path Planning, especially in the complex aspect, through the theoretical background of ACO and the other combination with other algorithms. The improved mathematics model evaluation demonstrates the upgrade steps in the path-finding process. Simulation results shows that the modified ACO algorithm is effective for complex environment compare to standard ACO algorithm. Moreover, the comparison results are presented in this paper between the standard ACO and modified ACO algorithm.

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Design of an easy upgradable cost efficient autonomous assistive robot ROSWiTHA

Design of an easy upgradable cost efficient autonomous assis...

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2022 international Conference on Electrical, computer, Communications and Mechatronics engineering, iCECCME 2022

作者： Sharan, Sudeep Nauth, Peter Umansky, Julian Michalik, Robert Domínguez-Jiménez, Juan José Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 Cádiz Puerto Real11519 Spain

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

in this paper, a design and development of an assistive robot are proposed that assists to the elderly and handicapped people, such as providing information and care with daily routine supports with the ability to perform various tasks, including the contact safety. Firstly a clear function requirement for such assistive robots was made. Secondly different sub-parts have been mounted to synthesize/unify the functions such as high power, dexterity/competence, mobility, safety and the cost-effectiveness. Additionally, a set of different applications, like the robot moves to other rooms/locations according to the commands given by the user, and the arm performs the task of grabbing and pouring/serving. Based on the application, the assistive prototype robot ROSWiTHA (RObot System WiTH Autonomy) was developed successfully, that operates using three major systems- the vision system, the arm system and the navigation system. The mechanisms are the camera, LiDAR, ultrasonic sensors, dual arm with gripper and two wheeled mobile vehicle including two castor wheels. Some experiments are performed focusing on assistive tasks using ROSWiTHA's sub-systems, indicating that the robot software can be integrated and become extremely useful to improve the living style of the elderly and handicap people in future. © 2022 iEEE.

关键词： Cost effectiveness

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Azerbaijani sign language recognition using machine learning approach 2

Azerbaijani sign language recognition using machine learning...

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2nd international Conference on Robotics and Artificial intelligence, RoAi 2021

作者： Aliyev, Samir Almisreb, Ali Abd Turaev, Sherzod Department of IT Infrastructure Solution R.I.S.K. Company BakuAZ1022 Azerbaijan Department of Computer Science and Engineering International University of Sarajevo Sarajevo71210 Bosnia and Herzegovina Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

Sign Language recognition is one of the essential and focal areas for researchers in terms of improving the integration of speech and hearing-impaired people into common society. The main idea is to detect the hand gestures of impaired people and convert them to understandable formats, such as text by leveraging advanced approaches. in this paper, we present our contribution to the improvement of Azerbaijani Sign Language (AzSL). We worked on AzSL Alphabet static signs real-time recognition. The method applied in this work is Object Classification and Recognition by leveraging pre-trained lightweight Convolutional Neural Networks models. At first, a dataset containing near to 1000 images has been collected, then interesting objects on images have been labeled with bounding boxing option. To build, train, evaluate and deploy the relevant model, TensorFlow Object Detection APi with Python has been employed. MobileNet v2 pre-trained model has been leveraged for this task. in the trial experiment with four sign classes (A, B, C, E) and 5000 step numbers 15.2% training loss and 83% evaluation mean average precision results have been obtained. in the next step of model deployment experiments with all 24 static signs of AzSL, 49700 and 27700 steps (180 and 100 epochs, respectively) 6.4% and 18.2% training losses, 66.5% and 71.6% mAP outcomes gained, respectively. © Published under licence by iOP Publishing Ltd.

关键词： Audition

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Envisaging an intelligent Blockchain Network by intelligence Sharing

Envisaging an Intelligent Blockchain Network by Intelligence...

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2022 international Conference for Advancement in Technology, iCONAT 2022

作者： Nayak, Arijit De, Sourav Bhattacharyya, Siddhartha Mukhopadhyay, Debarka Muhammad, Khan Gorbachev, Sergey Alumnus Software Limited Kolkata India Cooch Behar Govt. Engineering College Dept. of Computer Science Engineering Cooch Behar India Rajnagar Mahavidyalaya Birbhum India Department of Computer Science and Engineering Bangalore India Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of National Research Tomsk State University Department of Innovative Technologies Tomsk Russia

ISBN: (纸本)9781665425773

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. in this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. © 2022 iEEE.

关键词： Blockchain

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd international Conference on Cyber Resilience, iCCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for Ai to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 iEEE.

关键词： Discrete wavelet transforms

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MABPD: Mobile Agent-Based Prevention and Black Hole Attack Detection in Wireless Sensor Networks

MABPD: Mobile Agent-Based Prevention and Black Hole Attack D...

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Business Analytics for Technology and Security (iCBATS), international Conference on

作者： Humaira Ashraf Fizza Khan Uswa ihsan Fatima Al-Quayed N.Z. Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia School of Computer Science and Engineering (SCE) Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. in the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique.

关键词： Wireless communication Energy consumption Wireless sensor networks Mobile agents Energy measurement Clustering algorithms Transportation

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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Frontiers of information Technology (FiT)

作者： Khadim Hussain Muhammad Azhar Bumshik Lee Asma iqbal Muhammad Affan Sajid Ullah Khan Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong Department of Computer Science and Software Engineering Chosun University Korea Department of Computer Science Tsinghua University China Department of Computer Science University of Lakki Marwat

in this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named ‘ASAnalyzer’, which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COViD-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods.

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Cloud Architectural Design for image-based Vehicle Positioning in Traffic Management

Cloud Architectural Design for Image-based Vehicle Positioni...

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international Conference on System Theory, Control, and Computing (iCSTCC)

作者： iosif-Alin Beti Paul-Corneliu Herghelegiu Cristian-Mihai Amarandei Constantin-Florin Caruntu Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iaṣi Iasi Romania Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iaṣi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using image processing algorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.

关键词： Cloud computing Technological innovation 5G mobile communication image processing Scalability computer architecture Reliability theory Real-time systems Servers Autonomous vehicles

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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international Conference on Emerging Technologies, iCET

作者： Hassan Bin Khalid Abu Bakar Siddique Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. in addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain.

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

Empowering Donors: How Blockchain Technology Can Help Ensure...

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Business Analytics for Technology and Security (iCBATS), international Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. in this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol internet of Things Security

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