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检索条件"机构=Inst. of Computer Science and Engineering"

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LIVES: XAI-based Secure UAV Communication Framework for Essential Items Delivery

LIVES: XAI-based Secure UAV Communication Framework for Esse...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Ruparelia, Vidhi Shah, Sneh Pathak, Lakshin Gupta, Rajesh Tanwar, Sudeep Sawhney, Sahil Gupta, Ankur Nirma University Inst. of Technology Department of Electronics and Communication Engineering Ahmedabad India Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India Model Institute of Engineering & Technology Department of Computer Science & Engineering Jammu & Kashmir India

ISBN: (纸本)9798331527518

The application of Unmanned Aerial Vehicles (UAVs) for securely delivering essential items during extreme conditions includes numerous challenges, particularly in ensuring identification of potential threats. To solve this challenge, we propose a strong framework that makes use of Boosting algorithms, with a particular focus on XGBoost to enhance attack classification in UAV-based delivery systems. The XGBoost model achieved an accuracy of 93%, a precision of 0.9312, a recall of 0.9311, and an F1 score of 0.9283 , which makes it a highly suitable model for security of UAVs. Explainable AI (XAI) is used to further enhance classification including SHAP and LIME to identify the most influential features, such as the barometer and battery levels. Our framework provides a comprehensive solution for secure UAV-based deliveries. The experimental results proves the effectiveness of the proposed model which shows its potential for real-life applications. © 2025 IEEE.

关键词： Drones

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A Novel Approach for Detecting and Preventing Security attacks using Machine Learning in IoT 14

A Novel Approach for Detecting and Preventing Security attac...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Al Sukhni, Badeea Kumar Mohanta, Bhabendu Kumar Dehury, Mohan Kumar Tripathy, Asesh University of Westminster Department Cyber Security London United Kingdom Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Amity University Jharkhand Ranchi Amity Inst. of Information Technology India

ISBN: (纸本)9798350335095

Since most of the applications rely on machine learning algorithms, the customers are affected by the machine-made decisions. This can be resolved by adding more complexity to ML models. Common attacks that are possible to break the reliability of ML techniques are replay attacks, zero-dynamics attacks, and adversarial attacks. These attacks leverage the vulnerabilities of ML and collapse the reliability of data. The development of trusted ML systems can be achieved with the help of technical tools such as probabilistic modeling, Markov decision process. temporal decision process. Then, to achieve a reliable machine-learning framework, a simple way could be to utilize an ensemble for the regular models. This can develop reliable predictions. Another emerging technique is transfer learning that helps to overcome the performance loss and increases the trust score. However, estimating reliability will define the accuracy of predictions, it can work along with transductive reasoning. In general, reliability calculation is defined as the probability of count of true classes from the example set. © 2023 IEEE.

关键词： Reliability

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Optical Tomography of Clouds and Atmospheric Turbulence from Space and Ground-based Cameras

Optical Tomography of Clouds and Atmospheric Turbulence from...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Schechner, Yoav Y. Shaul, Nir Holodovsky, Vadim Aides, Amit Schilling, Klaus Koren, Ilan Viterbi Faculty of Electrical & Computer Engineering Technion - Israel Inst. of Technology Haifa32000 Israel Zentrum für Telematik Magdalene-Schoch-Str. 5 Würzburg97074 Germany Department of Earth & Planetary Sciences Weizmann Institute of Science Rehovot7610001 Israel

Novel tomographic principles yield 3D atmospheric fields using multi-view imagery. Turbulence strength is mapped by observing scintillation of bulbs. Extinction in clouds is mapped volumetrically from polarimetric cam... 详细信息

关键词： Optical tomography

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on inst.uction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute inst.uctions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

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Tailored Output Layers of Neural Networks for Satisfaction of State Constraints in Nonlinear Control Systems

Tailored Output Layers of Neural Networks for Satisfaction o...

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American Control Conference (ACC)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Engineering and Computer Science Inst. of Control and System Theory University of Kassel Germany

This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.

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Prediction of Continuous Targets by Explainable Imbalanced Regression from Omics Data in Childhood Obesity 1

Prediction of Continuous Targets by Explainable Imbalanced R...

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1st Workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

作者： Arteaga, María Torres-Martos, Álvaro Anguita-Ruiz, Augusto Alcalá-Fdez, Jesús Alcalá, Rafael Gacto, María José Dept. of Computer Science and Artificial Intelligence Univ. of Granada Granada Spain Center of Biomedical Research Univ. of Granada Inst. de investigación Biosanitaria ibs.GRANADA Granada Spain Inst. de Salud Carlos III Madrid Spain Barcelona Spain Univ. of Granada Granada Spain Dept. of Software Engineering Univ. of Granada Granada Spain

Childhood obesity is a persistent challenge for society since it is highly related to insulin resistance and a wide range of other chronic diseases, which impair not only the health of the people, but also the health system itself due to extra costs of treating them. In this context, it is imperative to consider innovative approaches, like eXplainable Artificial Intelligence, to understand the factors underlying childhood obesity. Data imbalance has been thoroughly studied in classification, but scarcely studied in regression since classes do not exist. However, extreme values in continuous domains, which are usually minority are often the most clinically relevant. Facing the imbalance in regression while obtaining explainable models has never been studied. In this application work, we adopt a Machine Learning approach to obtain explainable regression models for imbalanced continuous targets in childhood obesity, as HOMA-IR and Waist Circumference. We consider 79 variables, including targeted metabolomics, targeted proteomics, exposomic data (e.g., the physical activity subdomain), hematological parameters and anthropometry of Spanish children from 3 to 18 years with significant imbalance ratios around 12% in HOMA-IR and Waist Circumference. Even though we mainly focus on extensions of linguistic fuzzy rule-based systems, particularly designed for explainability, we also consider highly accurate complementary approaches as Random Forest that could additionally provide contrast interesting information by post-hoc SHAP. The models so obtained are better considering the relevant minority information (17% improvements in F1). Moreover, they seem to properly explain biologically meaningful relations, as in the case of physical activity data or the one with the follicle stimulating hormone, among others. © 2024 Copyright for this paper by its authors.

关键词： Nutrition

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A Study on a New Pseudorandom Number Generator with Deterministic Maze Construction

A Study on a New Pseudorandom Number Generator with Determin...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Takeru Miyazaki Shunsuke Araki Satoshi Uehara Faculity of Management and Information Sciences Kyushu Inst. of Information Sciences Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan Faculty of Environmental Engineering The Univ. of Kitakyushu Fukuoka Japan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In this paper, we propose a new pseudorandom number generator (PRNG), which uses a new deterministic maze construction for our maze-based PRNG [1]. We evaluate some relations between a size and a number of shapes for the mazes generated by our maze construction. Then, we also estimate a number of shapes and the maximum duplicate number of mazes by the size of the maze. By using the asymptotic equations of these estimated values, we can derive the minimum required maze size for the required number of sequences in which most or all sequences are different.

关键词： Shape Generators Consumer electronics

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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques

A Method for Predicting Cardiovascular Disorder using Machin...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Pavithra, D.R. Shivashankara, S. Prasanna Kumar, G. Soumyasri, S.M. Nagashree, S. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Belagavi India Jss Science and Technology University Sri Jayachamarajendra College of Engineering Mysuru India Kr Pete Krishna Government Engineering College Krishnarajapet India The National Institute of Engineering Mysore India Dept. of Computer Applications Vidya Vikas Inst. of Engg. and Tech. Affiliated to Visvesvaraya Technological University Belagavi India Vidya Vikas Institute of Engineering and Technology Mysuru India

ISBN: (纸本)9798350388602

Drastic increase of cardiovascular disease has led to a lot of adult's death. As per 'News 18' very year, 12 million young people in India die from heart disease. In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time. Second, most victims are under the age of 45. According to Dr. Maninder Sandhu, cardiologist and cardiologist at Artemis Hospital Gurgaon, 30 percent of people who suffer from heart disease are under the age of 45. This paper proposes a method that maybe used by laboratory technicians, doctors and users to understand whether there are any chances of having cardiovascular disease or not. The proposed system takes live data of patients and their lifestyle attributes these data are processed by the trained model to produce the output. In this model we haveused random forest classifier, linear regression, k-neighbors classifier & x gradient boost classifier. This model has attained maximum accuracy and least type one error in 'X- Gradient Boost'. We have concentrated more on type one error. The developed system shows whether the patient has any chances of cardiovascular disease or not. © 2024 IEEE.

关键词： Random forests

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Visual Analysis of Spinels with General Line Coordinates

Visual Analysis of Spinels with General Line Coordinates

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International Conference on Information Visualisation (IV)

作者： Leandro Luque María Luján Ganuza Ernesto Bjerg Boris Kovalerchuk Department of Computer Science and Engineering (UNS) Inst. for Computer Science and Eng. (CONICET-UNS) Bahia Blanca Argentina Department of Geology (UNS) INGEOSUR (UNS-CONICET) Bahia Blanca Argentina Department of Computer Science Central Washington University Ellensburg United States

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

In the geological context, the analysis of multidimensional data is a very common task and requires visualization techniques for exploration. General Line Coordinates (GLC) is a technique especially intended for lossless representation among the various techniques available for the visualization of multidimensional data. In this study, the application of GLC for pattern analysis and identification in mineral datasets is investigated. Furthermore, we develop a web-based tool to explore the possibility of employing GLC to leverage these approaches to derive visual discovery rules for pattern recognition.

关键词： Visualization Data visualization Pattern recognition Minerals Pattern analysis

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Adverse Event Prevention on The Road System with Collaborative MEC

Adverse Event Prevention on The Road System with Collaborati...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ru-Jun Wang Han-Rong Lai Shih-Jui Wang Yu-Hsun Kuo Chih-Hang Wang Wen-Tsuen Chen De-Nian Yang Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Inst. of Communications Engineering National Tsing Hua University Hsinchu Taiwan Inst. of Information Science Academia Sinica Taiwan

The localization of Road Users (RUs) is an important issue in adverse event prevention due to the unreliable nature of GPS and the high cost of high-precision location acquisition sensors. In addition, previous research on adverse event prevention on roads has not taken into account RUs in blind spots at the same time. To address these issues, we investigate a collaboration system for heterogeneous RUs and Mobile Edge Computing (MEC) servers, called Collaborative Adverse Event Prevention system (CAEP) to efficiently alert RUs to potential adverse events and perceive the blind spot of the RUs. CAEP includes two AI-based functional modules, a localization module and a blind spot detection module, and an adverse event prevention algorithm. The localization module localizes each RU and the blind spot detection module detects the other RUs in the blind spot. The adverse event prevention algorithm jointly considers general road collision events and the event of a difference in radius between the inner wheels of a vehicle to completely include adverse events on the road. We implement CAEP in a real-world traffic environment with Jetson AGX Xavier devices and cameras to evaluate the performance. Our evaluation shows that CAEP provides RUs with sufficient preparation time to prevent adverse events and correctly detects the RUs in blind spots.

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