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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

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computer systems science & Engineering 2023年第8期46卷 2415-2428页

作者： Muhammad Rizwan Rashid Rana Saif Ur Rehman Asif Nawaz Tariq Ali Azhar Imran Abdulkareem Alzahrani Abdullah Almuhaimeed University Institute of Information Technology–PMAS Arid Agriculture University Rawalpindi46000Pakistan Department of Creative Technologies Air UniversityIslamabad44000Pakistan Computer Engineering and Science Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Digital Health Institute King Abdulaziz City for Science and TechnologyRiyadh11442Saudi Arabia

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

关键词： Aspects deep learning lexicon sentiments reviews

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Transparent Third-Party Authentication With Application Mobility for 5G Mobile-Edge Computing

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IEEE Transactions on Network and Service Management 2024年第1期21卷 1142-1157页

作者： Ali, Asad Lin, Ying-Dar Li, Chi-Yu Lai, Yuan-Cheng National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Taiwan University of Science and Technology Department of Information Management Taipei106 Taiwan

Mobile Edge Computing (MEC) is a key technology for supporting low latency applications close to the end user. Users can access application servers in MEC instead of routing to the Internet by passing through a core cellular network. Few security challenges arise as the traffic does not traverse through the core network, and these can be solved by providing authentication services in the MEC. However, authentication and application mobility issues arise in the case of multiple MECs where a user is mobile and needs continuous service from application servers, without needing to establish a new session and providing authentication information repeatedly to every new MEC the user connects with. In this work, we propose two solutions, a TC3A (Token-based Cookie transfer & 3rd-party Authentication) and a TS3A (Token-based State transfer & 3rd-party Authentication) for resolution of authentication and application mobility issues while achieving low latency. We conducted experiments on a testbed that had MECs deployed in a real-time cellular network (emulated via OpenAirInterface) and performed user handover between two MECs. The experimental results show that TC3A and TS3A successfully re-authenticate the users, without provision of login credentials with target MEC, while reducing the latency by approximately 49.76-59.72% as compared to simple login method. The TC3A and TS3A also eliminate the need of keeping multiple accounts for applications at different MECs and most importantly provide application service continuity, through state transfer during cross-system handover, which is not provided by a simple login method. TC3A provides the application service continuity without any loss of session state, which is suitable for applications that cannot afford state loss, and TS3A provides the same while reducing the latency by 47.05-51.25% as compared to TC3A, which is suitable for applications that require low latency. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Mutual character dialogue generation with semi-supervised multitask learners and awareness

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International Journal of information technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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Contextual Reinforcement Learning for Unsupervised Deformable Multimodal Medical Images Registration 18

Contextual Reinforcement Learning for Unsupervised Deformabl...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zheng, Yang Xian, Hongjiang Shuai, Zhikun Hu, Jing Wang, Xin Hu, Shu Chengdu University of Information Technology Department of Computer Science China Department of Epidemiology and Biostatistics School of Public Health NY United States Purdue University Department of Computer and Information Technology IN United States

ISBN: (纸本)9798350364132

Multimodal deformable image registration refers to the process of finding the spatial correspondence between pairs of images with multimodal and mapping them onto the same coordinate system. Most of the deep learning-based registration methods are one-shot registration, which is difficult to handle images with significant deformations or displacements. Reinforcement learning can handle these challenges by viewing registration as a strategic decision-making process which is a step-by-step registration. However, it faces challenges with high-dimensional and continuous deformation fields. To overcome this, we introduce a planner network that maps high-dimensional input state to low-dimensional plan, guiding the actor to generate continuous actions. In order to handle complex multi-modal registration, we propose a multi-frame plan module which encourages artificial agent to explicitly utilize the redundant states in the registration process and learn more accurate registration actions from the generated state frames. To facilitate the training and convergence of the model, we define an unsupervised reward function and incorporate spectral normalization layers. The entire framework is a fully unsupervised registration framework and training in an end-to-end manner. We evaluated our method on publicly available T1w and T2w brain datasets, and the results indicate that our method has excellent deformable registration capability for multimodal images. © 2024 IEEE.

关键词： Image registration

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Analyzing Standardized Inverse Time-Current Curve Types of Overcurrent Relays for Efficient Overcurrent Protection in Distribution Networks 47

Analyzing Standardized Inverse Time-Current Curve Types of O...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Rojnić, Michele Prenc, Rene Dubravac, Marina Šimić, Zvonimir University Of Rijeka Faculty Of Engineering Croatia University Of Osijek Faculty Of Electrical Engineering Computer Science And Information Technology Croatia

ISBN: (纸本)9798350382495

The selection of the appropriate curve type of overcurrent relay function is significant for achieving optimal coordination of overcurrent protection in distribution networks. In this study, we conducted a comprehensive analysis of the overcurrent relays performance regarding their different inverse time-current curve types. Additionally, different fault locations were taken into consideration. To investigate the impact of curve types on overcurrent relays coordination, we employed a uniform curve type for all overcurrent relays in two representative power systems. The results, encompassing three standardized IEC curve types, are presented, and analyzed to illustrate the efficacy of our proposed approach. Therefore, this study underscores the critical role that curve type selection has in determining the feasibility of attaining reasonably low overcurrent relay trip times. © 2024 IEEE.

关键词： Relay protection

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Generation of High-Quality Relevant Judgments through Document Similarity and Document Pooling for the Evaluation of information Retrieval systems 14

Generation of High-Quality Relevant Judgments through Docume...

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14th International Conference on Software, Knowledge, information Management and Applications, SKIMA 2022

作者： Joseph, Minnu Helen Ravana, Sri Devi University of Malaya Faculty of Computer Science & Information Technology Department of Information Systems Malaysia Faculty of Computer Science Engineering & Technology Malaysia

ISBN: (纸本)9781665493345

The information Retrieval system Evaluation have carried out through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of document corpus, topics, and a set of relevance judgements. The relevant judgements are the documents which retrieved from the test collections based on the topics. The precision of the evaluation process is based on the number of relevant documents in the relevant judgement list called qrels. This paper presents a study on how methodologies like pooling and document similarity helps to generate more relevant documents into the relevance judgments set in order to increase the accuracy of the evaluation process. The initial results have shown that combination of pooling with document similarity performs better compared to base clustering or classification. © 2022 IEEE.

关键词： information retrieval systems

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