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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Evren Dağlarlı Erke Arıbaş Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

In this paper, the design of fixed-wing autonomous UAVs is aimed. The goal is to lock onto rival UAVs during the competition, avoid these locks with aggressive maneuvers, and perform the designated tasks autonomously. Autonomous Aircraft System; It consists of a ground control station, ground antenna, controller, and autonomously operating aircraft components. In the declaration, the system’s performance features, mechanical and aerodynamic design, flight control systems and communication systems are detailed. In addition, the UAV’s autonomous locking, image processing and task performance capabilities are presented, supported by tests and analysis. The final system architecture and performance analysis results show that the UAV meets the design requirements.

关键词： Communication systems Image processing Systems architecture Process control Autonomous aerial vehicles Data processing Control systems Performance analysis Aircraft Aerospace control

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Aspect-Based Subjectivity Analysis Using a BERT-based Approach

Aspect-Based Subjectivity Analysis Using a BERT-based Approa...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Ng, Hu Chong, Wing Kin Chia, Yu Zhang Vun Yap, Timothy Tzen Goh, Vik Tor Wong, Lai Kuan Teck Tan, Ian Kim Cher, Dong Theng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia School of Mathematical & Computer Sciences Heriot-Watt Univeristy Putrajaya Malaysia SIRIM Berhad Shah Alam Malaysia

ISBN: (纸本)9798350353464

Aspect-based subjectivity analysis stands as an important task in natural language processing, seeking to identify the subjectivity of various aspects or features within a text. A new method for aspect-based subjectivity analysis using BERT is introduced in this paper. BERT has demonstrated impressive performance across various NLP tasks, and its capabilities are utilized to accurately ascertain the subjectivity of specific aspects within a given text. The approach involves fine-tuning BERT on a sizable dataset annotated with aspect-level subjectivity labels, enabling the model to grasp the subtleties of aspect-based subjectivity analysis. Extensive experiments on benchmark datasets are conducted to showcase the effectiveness of this approach and compare it with existing methods. The results reveal that this proposed approach surpasses state-of-the-art techniques in aspect-based subjectivity analysis, underscoring the potential of leveraging BERT for such purposes. ©2024 IEEE.

关键词： Aspect-based NLP Subjectivity Analysis

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Leveraging Channel Capacity of a Macro Diversity MIMO System in Gamma-Shadowed Nakagami-m Fading Channel for QoS estimation using Quantum Machine Learning 30

Leveraging Channel Capacity of a Macro Diversity MIMO System...

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30th Telecommunications Forum, TELFOR 2022

作者： Suljovic, Suad Vujovic, Vuk Markovic, Filip Milic, Dejan Petrovic, Nenad Lazarevic, Angelina Academy of Technical Vocational Studies Applied Engineering Sciences Belgrade Serbia MB University Computer Science and Informatics Information Technology Belgrade Serbia University of Prišrina Faculty of Technical Sciences Computer Science and Informatics Mitrovica Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Faculty of Electronic Engineering Computer Science and Informatics Niš Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

In this paper we consider a system of one macro diversity SC receiver (MD SC) and two micro MRC (mD MRC) receivers, operating in a Nakagami-m multipath environment with gamma shadowing. The combination of the maximum L-branch SNR signal-to-noise ratio is realized at the micro level, and the selector combiner (SC) with two base stations is realized at the macro level. A closed-form expression for the channel capacity (CC) of the envelope of the SC macro diversity output signal is calculated. Graphical results show the effects of different system parameters on system performance, as well as the improvement due to the use of a combination of the micro and macro diversity. The derived expressions are used within GPU-based mobile network for modeling, planning and simulation of the system to estimate the quality of service (QoS). © 2022 IEEE.

关键词： Machine learning

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Yusuf Sulistyo Nugroho Salsabil Aisyah Abdul Halim Syful Islam Yogiek Indra Kurniawan Tati Erlina Informatics Engineering Faculty of Communication and Informatics Universitas Muhammadiyah Surakarta Surakarta Indonesia Computer Science and Engineering Faculty of Engineering BSMRSTU Gopalganj Bangladesh Informatics Engineering Faculty of Engineering Universitas Jenderal Soedirman Purwokerto Indonesia Computer Engineering Faculty of Information Technology Universitas Andalas Padang Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users ($\mathbf{7 2. 5 2 \%}$), followed by mid-reputation users $(\mathbf{1 7. 9 3 \%}$), and high-reputation users $\mathbf{(9. 5 5 \%)}$. The distribution of the unanswered question types shows that low-reputation users ask more “how” type of question ($\mathbf{3 8. 9 0 \%}$) followed by mid and high-reputation users. In contrast, the “what” and “why” questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement.

关键词： Technological innovation Information sharing System integration Manuals Encoding Labeling Information technology Faces Python Software development management

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Unveiling the Black Box: Investigating the Interplay between AI Technologies, Explainability, and Legal Implications 8

Unveiling the Black Box: Investigating the Interplay between...

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8th International Conference on computer Science and engineering, UBMK 2023

作者： Ardoganyilmaz, Cihan Mengunogul, Berkay Balci, Muhammet Marmara University Computer Engineering Department Istanbul Turkey Northeastern University Informatics Massachusetts United States Bahcesehir University Law Faculty Istanbul Turkey

ISBN: (纸本)9798350340815

Discovering patterns is the process of understanding, while explainability refers to the ability to represent the discovered patterns in a way that the target audience can comprehend. If we adapt the relevant proposition to explainability in law, jurists initially extract the essential components from a concrete case and then establish meaningful connections among these components to reach a conclusion. The second stage, where they explain how they reached the conclusion through the connections established between the components, is referred to as generating reasoning. This study focuses on the first stage of explainability, which involves extracting significant components and establishing basic-level connections with the conclusion. The proposed methodology presents an explainability approach that is independent of the model and can be applied to all classification problems. Furthermore, the best performance in the literature is achieved on the task of predicting the outcomes of Turkish Constitutional Court decisions. © 2023 IEEE.

关键词： Artificial intelligence

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Towards A Knowledge Graph-based Exploratory Search for Privacy engineering 8

Towards A Knowledge Graph-based Exploratory Search for Priva...

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8th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs, VOILA! 2023

作者： Herwanto, Guntur Budi Ekaputra, Fajar J. Piroi, Florina Sabou, Marta Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Institute of Information Systems Engineering Faculty of Informatics TU Wien Austria Institute of Data Process and Knowledge Management WU Wien Austria

The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and software development artifacts required to implement this concept can be a major burden for organizations. To address this challenge, we propose a knowledge graph-based Exploratory Search system to help organizations complete privacy engineering tasks. We identify major requirements of such systems and develop an Exploratory Search prototype built on privacy engineering knowledge that integrates different information sources. While still preliminary, this work serves as a foundation for future research on integrating privacy knowledge into software development and demonstrates the potential of Exploratory Search Systems to support the cognitive process in privacy tasks such as privacy threat identification. © 2023 Copyright for this paper by its authors.

关键词： Knowledge graph

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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