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作者： Mahfooz Ul Haque, Hafiz Saleem, Kiran Salman Khan, Ahmad Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science University of Lahore Lahore Pakistan

A natural disaster is an inevitable situation that can occur at any time and anywhere along with varied forms such as earthquakes, floods, hurricane, wildfire, and so forth, and different level of occurrences has been recorded from mild to an intense level. Timely disaster response plays an important role in reducing its deteriorate after-effects and can save countless lives. Over the years, people have been developing guidelines and processes to cope up with such kinds of hazardous situations. In recent years, with the advent of the pervasive computing paradigm, situation-awareness has been considered to be the most fascinating approach for situation assessment and provides decision support accordingly. Situation-aware systems observe/perceive dynamic changes in the environment, understand/comprehend the situation, and perform actions according to the environment. Although state-of-the-art formalisms have been developed to handle such kinds of hazardous situations intelligently and rescue the victims. However, belief-desire-intention (BDI) reasoning mechanism with the incorporation of situation-awareness is still the thirsty area of research to manage hazardous situations. In this article, we propose a temporal epistemic situation-aware formalism for BDI reasoning agents to model the context-aware decision support system. To demonstrate the work of the proposed formalism, we develop a case study based on a disaster situation, in which BDI agents are modeled and simulated to present the results in terms of agents' reasoning processes. We demonstrate the scalability in temporal aspects of the system using different levels of disasters to monitor the hazardous situations and evaluate the overall behavior of the system. © 2021 John Wiley & Sons Ltd.

关键词： Multi agent systems

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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Approach of Different Classification Algorithms to Compare in N-gram Feature Between Bangla Good and Bad Text Discourses 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Bitto, Abu Kowshir Bijoy, Md. Hasan Imam Khan, Saima Mahmud, Imran Biplob, Khalid Been Badruzzaman Department of Software Engineering Daffodil International University Dhaka1207 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1207 Bangladesh

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Bangla Natural Language Processing (BNLP) is a newish challenge in Artificial Intelligence. With the rapid expansion of the Bangla language, it is now adopted on a variety of platforms, including social media, communication platforms, news media, and so on. The classification of text documents becomes an important factor in resolving the challenge of information organization and knowledge management. This study uses five supervised classification methods to explore the categorization of Bangla text discourse using N-gram (unigram, bigram, and trigram) features. Bangla text discourse is collected from different platforms such as social media, personal Bangla blogs, and people's utterances in order to accomplish the research goal. After collecting data, the most difficult part of the Bangla language preprocessing is completed, which includes adding contractions, removing punctuations, encoding, and a variety of other operations. For this study, 1499 text documents were initially used, with 1459 Bangla text discourses being used after preprocessing. To convert the text into a token, N-gram feature methods utilizing TF-IDF-Vectorizer are used. During the experiment phase, unigram, bigram, and trigram feature techniques are used to apply Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest (RF), Multinomial Naive Bayes (MNB), and K-Nearest Neighbors (KNN) models to the dataset. In the unigram and bigram features, Multinomial Naive Bayes (MNB) outperformed all other classifiers, with the highest accuracy of 89.31% and 86.94%, respectively. The trigram feature of K-Nearest Neighbors (KNN) achieves a maximum accuracy of 84.25%, and the proposed model can classify the Bangla text document as Good or Bad Discourse. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge management

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Decision support system of staff member promotion based on cloud computing (Case Study University Of Benghazi) 3

Decision support system of staff member promotion based on c...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Alfsai, Entesar H. Bozed, Kenz A. Hagal, Mohamed A. University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350319897

The scientific council at Benghazi University suffered from issues related to the scientific promotion process for faculty members. Promotion is still done manually, without a computerized system. Therefore, it faces obstacles in obtaining basic information, issuing promotion decisions, making managerial procedures, lack of flexibility, and the accumulation of papers. There are also unclear biases in decision-making and mechanisms of evaluating promotion criteria. Thus, they need to keep up with the international universities in the promotion process to be successful in administrative business today. Benghazi University needs an information system that can support decision-making, which is a major problem often occurs in the process of promotion of faculty *** use of decision support systems is a solution to reduce subjectivity in decision-making. This work presents an E-Gate that can assist in the promotion of educational staff. It is a system able to provide the best alternative in promotion educational staff. With the decision support system, the promotion process is carried out via the E-Gate using the AHP method according to several criteria. It is stored in the cloud computing because many enterprises are adopting the cloud-computing platform to examine the benefits of self-service, user guide, and high flexibility. The time is right for the educational universities to derive benefits of the cloud, as they are obliged to deliver better ubiquitous services. The study also developed Search Engine to verify the submitted research. © 2023 IEEE.

关键词： Decision support systems

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An Empirical Evaluation of the Effectiveness of Spider and Proxy Modes for Web Security Testing 10

An Empirical Evaluation of the Effectiveness of Spider and P...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Chiu, Kuan-Wei Yang, Shih-Sheng Lee, Shin-Jie Lee, Wen-Tin National Cheng Kung University Department of Computer Science and Information Engineering Taiwan National Kaohsiung Normal University Department of Software Engineering and Management Taiwan

ISBN: (纸本)9798350304770

Spider and proxy modes are two commonly employed methods supported by dynamic application security testing (DAST) software. Despite efforts to enhance the automated spider's efficiency, deep exploration of web applications is still constrained by the need for manual intervention in certain complex UI operations. In this regard, the proxy mode serves a crucial intermediary, intercepting and inspecting request messages exchanged between the browser and the web application during manual or scripted browsing activities. This study aims to assess the efficacy of these two modes in terms of code coverage and the number of requests, utilizing two popular PHP-based open-source web applications. The experimental findings demonstrate that employing a hybrid mode (Spider-Last) yields a significant improvement compared to using the spider or proxy mode independently. © 2023 IEEE.

关键词： software testing

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Automatic Positioning of Intelligent Mobile Robot Based on Improved Front-End Scanning Matching

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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Synthesis of Agent-Based Decision-Making Systems with Multiple Coalitions

Synthesis of Agent-Based Decision-Making Systems with Multip...

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2023 International Conference on Electromechanical and Energy Systems, SIELMEN 2023

作者： Ababii, Victor Sudacevschi, Viorica Munteanu, Silvia Carbune, Viorel Melnic, Radu Lasco, Victor Technical University of Moldova Department of Computer Sciences and Systems Engineering Chisinau Moldova Technical University of Moldova Department of Software Engineering and Automatics Chisinau Moldova

ISBN: (纸本)9798350315240

This paper presents a synthesis method of decision-making systems based on Agents forming multiple coalitions. The condition for coalition formation is defined by a mathematical model that solves the problem of global optimization of the objective function. The problem of synthesizing the agent-based system was formulated, and the mathematical model for negotiating the coalition formation process was developed. The process of initiation, dialogue and coalition formation is presented based on the sequence diagram. For structural modeling, Membrane Computation (P-Systems) models are used that highlight the parallelism and topology of the Multi-Agent system. The Agent structure is defined as a collection of knowledge (data and processing methods) and input/output ports. The functional validation of the Agent and the Multi-Agent system was performed based on the Timed Petri Nets. © 2023 IEEE.

关键词： Petri nets

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System for Table Run Strategy in Peruvian Billiards with Real-Time Oject Detection using YOLOv8n 10

System for Table Run Strategy in Peruvian Billiards with Rea...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Silva, Italo Chavez, Raquel Castillo-Sequera, José Luis Wong, Lenis Peruvian University of Applied Sciences Department of Software Engineering Lima Peru University of Alcalá Department of Computer Science Alcalá de Henares Spain

ISBN: (纸本)9798350387353

The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as 'closing the table.' This strategy requires high precision and consistency, especially for less experienced players. This study integrates a YOLOv8n model, a shot suggestion algorithm, and a shot prediction algorithm to build a system that aids players in developing this 'table closing' strategy through visual guides indicating the shots to take and where to position the cue ball for the next shot. The proposed system follows these phases: (1) Selection of the video camera, (2) Selection of the object detection technique, (3) Development of the shot suggestion algorithm, (4) Development of the shot prediction algorithm, and (5) System construction. Phases (1) and (2) address the implementation of the YOLOv8n model, while phases (3) and (4) involve the development of algorithms for the 'table closing' strategy. To validate the system's effectiveness, an experiment with 24 players of different skill levels was conducted, showing that the system reduced the average number of shots needed to close a table to 22, compared to the 47-shot average of professional players. Beginners accepted 100% of the system's recommendations, while intermediate and professional players showed acceptance rates of 95.1% and 95.6%, respectively. A survey indicated high overall satisfaction, with a median response of 4.5 on a 5-point scale. The system also demonstrates significant competitive advantages over existing solutions by maintaining high detection and prediction accuracy while effectively functioning in real-world scenarios, leveraging the latest advances in computer vision to surpass older techniques in both effectiveness and efficiency. © 2024 IEEE.

关键词： computer vision

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Forecasting the Stability of COVID-19 Vaccine Companies Stock Market using LSTM and Time-series Models 3

Forecasting the Stability of COVID-19 Vaccine Companies Stoc...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Rahemeen Baloch, Safia Bahria University Department of Software Engineering Karachi Pakistan Fast University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350332391

Stock price forecasting is challenging because stock markets are volatile and nonlinear in nature. With the impact of the Covid-19 outbreak in 2019, stock market forecasting has become more complicated. The goal of this study is to identify the top brands in the global covid-19 vaccination market and their impact on the stock market. By forecasting the next months stock exchange, traders will be able to select the most suitable company to invest in. In this paper, MSE, RMSE, and MAPE are used as evaluation matrices to compare timeseries and neural network models such as Arima, Prophet, and LSTM. According to the results of a comparative analysis, the LSTM model produced the most accurate predictions on majority stock prices dataset for the selected vaccination brands. Brand stability is achieved by predicting future stock exchange prices using an outperformed LSTM model. As a result, BioNTech has been identified as a high potential stable brand that the buyer can invest to gain enormous returns among the massive number of probabilistic events. © 2023 IEEE.

关键词： Time series

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Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images 1st

Tumor-TL: A Transfer Learning Approach for Classifying Brain...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Bitto, Abu Kowshir Bijoy, Md. Hasan Imam Yesmin, Sabina Mia, Md. Jueal Department of Software Engineering Daffodil International University Dhaka1341 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1341 Bangladesh

ISBN: (纸本)9783031346187

An intracranial tumor is another name for a brain tumor, is a fast cell proliferation and uncontrolled bulk of tissue, and seems unaffected by the mechanisms that normally govern normal cells. The identification and segmentation of brain tumors are among the most common difficult and time-consuming tasks when processing medical images. MRI is a medical imaging technique that allows radiologists to see within body structures without requiring surgery. The information provided by MRI regarding human soft tissue contributes to the diagnosis of brain tumors. In this paper, we use several Convolutional Neural Network architectures to identify brain tumor MRI. We use a variety of pre-trained models such as VGG16, VGG19, and ResNet50, which we have found to be critical for reaching competitive performance. ResNet50 performs with an accuracy of 96.76% among all the models. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network architecture

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