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SSRN 2022年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a twodimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-bypixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Performance Comparison of Machine Learning Models Powered by SHAP and LIME Based Explainability Techniques on Diabetes Dataset

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SSRN 2024年

作者： Güler, Hakan Avcı, Derya Ulaş, Mustafa Omma, Tülay Software Engineering Faculty ofTechnology FıratUniversity Elazığ Turkey Computer Technologies Vocational School of Technical Sciences Fırat University Elazığ Turkey ArtificialIntelligenceandDataEngineering FacultyofEngineering FıratUniversity Elazığ Turkey Department of Endocrinology and Metabolic Diseases Faculty of Medicine Lokman Hekim University Ankara Turkey

Early diagnosis of diabetes can increase patients' quality of life and improve treatment processes. In this context, this article focuses on the early diagnosis and prediction of diabetes, addressing the performance of various machine learning models and the role of explainable artificial intelligence (XAI) techniques. With the rise of large datasets in the healthcare industry, data mining and machine learning techniques have become an important tool for the discovery and analysis of diabetes datasets spanning healthcare systems. This study investigates a diabetes dataset that includes healthcare systems. Various machine learning models such as K-NN, SVM, Naive Bayes, CNN, Decision Tree, Random Forest and XGBoost were evaluated on this data set and their performances were compared. Visualizing the overall structure of the data set is important for analyzing relationships between diabetes-related features. The article starts with cleaning the dataset and preprocessing steps, followed by the training and testing phases of each model on the dataset. Each model was evaluated based on success criteria such as accuracy, F1 score, sensitivity, and specificity. In addition, the understandability of the model's decisions was increased by applying explainable artificial intelligence (XAI) methods, SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to the outputs of the most successful model. These techniques explain the internal working mechanism of the model by determining which features have the most impact on model outputs. The analyzes were supported by expert doctor's comments and the potential of the models in real world applications was highlighted. When the models and results are examined, respectively;it can be seen that the results of K-NN: 81.18%, SVM: 75.38%, Naïve Bayes: 75.49%, CNN: 74.83%, Decision Tree: 76.91%, Random Forest: 91.68%, XGBoost: 98.91% are obtained. As a result, machine learning models effectively demo

关键词： Decision trees

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Enhancing Monkey to trigger malicious payloads in Android malware 17

Enhancing Monkey to trigger malicious payloads in Android ma...

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17th International ISC Conference on Information Security and Cryptology, ISCISC 2020

作者： Hasan, Hayyan Ladani, Behrouz Tork Zamani, Bahman University of Isfahan Faculty of Computer Engineering MDSE Research Group Department of Software Engineering Isfahan Iran

ISBN: (数字)9781728185699

ISBN: (纸本)9781728185699

Dynamic analysis is a prominent approach in analyzing the behavior of Android apps. To perform dynamic analysis, we need an event generator to execute the app. Monkey is the most popular event generator that is used in Android dynamic analysis. Monkey provides high code coverage, and yet high speed in generating events. However, in the case of malware analysis, Monkey suffers from several limitations. It only considers UI events but no system events. Moreover, it causes disconnecting the connectivity of the test environment during the analysis process. In this paper, we try to enhance Monkey to reduce its limitations while preserving its advantages. The proposed approach includes preparing Monkey with a facility for handling system events and keeping the connectivity of the test environment up during the analysis process. To evaluate the extended version of Monkey, we compare it with its original version regarding two important criteria in the case of malware analysis: The number of called sensitive APIs, and the code coverage. The evaluation process uses 100 randomly selected samples from AMD malware dataset. The results show that enhanced Monkey improves its ability to trigger sensitive APIs, and increases its code coverage. © 2020 IEEE.

关键词： Android (operating system)

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Depth Based Region Proposal: Towards Robust Two-Stage Real-Time Object Detection

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SSRN 2022年

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-by-pixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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Games for Scrum Team Collaboration in the Global software Development Environment: A Literature Review

Games for Scrum Team Collaboration in the Global Software De...

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International Conference on Informatics and Computing (ICIC)

作者： Anita Hidayati Iklima Ermis Ismail Ade Rahma Yuly Henry Edison Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Software Engineering Blekinge Institute of Technology Sweden Swedia

ISBN: (纸本)9798350345728

software development companies commonly use Global software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies.

关键词： Industries Systematics Bibliographies Games software Teamwork Object recognition

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Notes on the Architecture, League Training and PFSP in AlphaStar 16

Notes on the Architecture, League Training and PFSP in Alpha...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Zhao, Jianyang Zhou, Ningning Hong, Long Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

AlphaStar, a multi-agent intelligence program, is a new member of Alpha's gamer software. Rated at Grandmaster level in the empirical evaluation shows that AlphaStar has been a great success in engineering, which is another milestone in AI history. After a brief introduction to AlphaStar, we discussed its computational architecture, league training, PFSP, and proposed an interesting topic direction on the basis of our suggestions, which was how to combine the capacities of diverse ANNs that successful completed learning. Additionally, we proposed the viewpoint that is big data - space, intelligence - man' world, parallel computing - time. © 2021 IEEE.

关键词： Multi agent systems

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Haram Kamal Bakhtawar Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Muhammad Atif Yaqub Hooria Umar Hoor Fatima Usama Nasir Computer Engineering Department Information Technology University Lahore Pakistan Department of Computer Science Bahria University Faculty of Information Technology University of Central Punjab Lahore Department of Artificial Intelligence University of Management & Technology Lahore Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Software Developer Pakistan Bahria University

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis.

关键词： Heart Support vector machines Training Accuracy Machine learning algorithms Predictive models Prediction algorithms Reliability Diseases Testing

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Sentiment Analysis for Product Reviews Based on Deep Learning 2

Sentiment Analysis for Product Reviews Based on Deep Learnin...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Xiao, Shaozhang Wang, Hexiang Ling, Zhi Wang, Lanfang Tang, Zhaoxia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu223003 China

With the popularity of the Internet and e-commerce, the sentiment analysis of text can help users to quickly and accurately obtain effective information they are interested in from massive product reviews to purchase satisfactory products. In this paper, a sentiment analysis system for product reviews was designed based on deep learning, and the digital and electronic products on Jingdong Mall with at least 100,000 reviews were crawled as the training data set. After data pre-processing operations such as word segmentation and removal of stop words for product reviews to remove useless features, the feature vectors were constructed based on the bag of words model and word2vec method, and then three classification algorithms, namely LSTM, Naive Bayes and logistic regression were used to model reviews. The LSTM algorithm is significantly superior to Naive Bayes and logistic regression algorithm in the training stage, and provides a reliable reference for the analysis of product reviews. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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Information geometry of bosonic Gaussian thermal states

arXiv

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arXiv 2024年

作者： Huang, Zixin Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on characterizing the distance between two nearby states and the geometry induced by a parameterization in terms of their mean vectors and Hamiltonian matrices. In particular, for the family of bosonic Gaussian thermal states, we derive expressions for their Fisher–Bures and Kubo–Mori information matrices with respect to their mean vectors and Hamiltonian matrices. An important application of our formulas consists of fundamental limits on how well one can estimate these parameters. We additionally establish formulas for the derivatives and the symmetric logarithmic derivatives of bosonic Gaussian thermal states. The former could have applications in gradient descent algorithms for quantum machine learning when using bosonic Gaussian thermal states as an ansatz, and the latter in formulating optimal strategies for single parameter estimation of bosonic Gaussian thermal states. Finally, the expressions for the aforementioned information matrices could have additional applications in natural gradient descent algorithms when using bosonic Gaussian thermal states as an ansatz. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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