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2024 International Conference on engineering and Computing, ICECT 2024

作者： Sumble, Malaika Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Fernandez-Rojas, Raul University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences & Tech. Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles. © 2024 IEEE.

关键词： Drones

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering Systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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Validation and evaluation of models in the study of characteristic descriptors based on patients' gender

Validation and evaluation of models in the study of characte...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Marinova, Ginka Todorova, Maya Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

Cancer-related distress is the result of the psyche's reaction to the diagnosis given and to the various stressors in the development of the type of malignant disease. The spreading of the psychological distress and depression may change depending on the stage, gender, and age of the patient. Identifying gender-based distress is important for improving the work of health professionals. The results of the study of models of characteristic descriptors based on patients' sex evaluated distress by applying three methods of machine learning. This report evaluates the effectiveness of the metrics accuracy and classification error. It implemented validation of models of descriptors and the calculated correlation coefficients. The results obtained for the two metrics - accuracy and classification error - were high. © 2022 IEEE.

关键词： Machine learning

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Leveraging a Hierarchical Selection of Communities in Social Networks to Maximize Diffusion 9

Leveraging a Hierarchical Selection of Communities in Social...

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9th International Conference on Web Research, ICWR 2023

作者： Heidari, Mohsen Fasahat, Mohamad Ali Afsharian, Sanaz Artishdar, Parinaz Esfarayen University Dept. of Electrical Computer and Industrial Engineering North Khorasan Iran Mehr Alborz Higher Education Institute Dept. of Computer Science Tehran Iran Islamic Azad University Damavand Branch English Department Tehran Iran Islamic Azad University Yadegar Emam Khomeini Branch Dept. of Software Engineering Tehran Iran

ISBN: (纸本)9798350399691

For businesses, discovering key nodes in social networks has a high computational overhead and cost. Because of this, in the problem of influence maximization, at least key nodes with the most influence in social networks are chosen at the lowest possible cost. Numerous algorithms have been proposed to find key nodes in social networks using the technique of influence maximization under the characteristics of submodularity and monotonicity;however, these algorithms struggle with the issue of optimal influence spread because some graph characteristics are ignored when considering diffusion and influence spread in the social network. The HSMD method, which gives special weight to the structure of communities and selects key nodes hierarchically utilizing the structure of communities, has been presented as a solution to this challenge. Based on the characteristics and strategic placement of the nodes within each community, weight is given to each community in this algorithm. Seed nodes are then chosen hierarchically using BFS traversal. This method enhanced the influence spread rate and execution time of recently introduced algorithms such as CTIM, IMBC, RNR, HEDVGreedy, and k-shell. © 2023 IEEE.

关键词： Diffusion

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AMN: Attention-based Multimodal Network for Android Malware Classification

AMN: Attention-based Multimodal Network for Android Malware ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Yi Yang Huyu Wu Yanzhe Wang Peng Wang Dept. Software Engineering Sichuan University Chengdu China Dept. Computer Science Sichuan University Chengdu China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

With the rapid evolution of malware, android malware classification has become increasingly important and remains an active area of research. Traditional machine learning methods rely heavily on hand-crafted features based on expert domain knowledge, making the feature engineering process time-consuming. In recent years, deep learning-based Android malware classification has attracted considerable attention, inspired by the remarkable success of deep learning techniques in the fields of vision and natural language processing. Deep learning methods replace the manual process of feature engineering with a framework capable of simultaneous feature extraction and classification directly from raw data. However, existing approaches do not fully exploit hybrid and multimodal features, which limits their ability to consider multiple disparate sources of information during classification. In this paper, we present attention-based multimodal network(AMN), a multimodal approach to malware classification based on the attention mechanism. AMN combines two modalities of data: 1) grayscale images generated by malware binary files, and 2) network traffic feature sequences. The model employs a cross-modal attention mechanism that combines the features of both modalities by compensating for their limitations. Furthermore, a transfer learning method is proposed to improve the generalization performance of the model, especially in scenarios with limited sample size. The proposed solution surpasses the state-of-the-art precision, achieving 97.73%, along with a recall of 96.51%, as demonstrated through experiments conducted on the CICInvesAndMal2017 dataset. Additionally, to show the effectiveness of our model, ablation experiments were conducted.

关键词： Deep learning Attention mechanisms Mechatronics Transfer learning Random access memory Telecommunication traffic Feature extraction

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Optimized Crop Detection Using IoT and Deep Neural Networks 5

Optimized Crop Detection Using IoT and Deep Neural Networks

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Choudhury, Tanupriya Rohini, A. Mahdi, Hussain Falih Uttarakhnad Dehradun248007 India Graphic Era Hill University Dehradun248002 India Dept. of Computer Science & Engg Visakhapatnam531162 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350337525

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system. © 2023 IEEE.

关键词： Crops

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Towards An Automatic Construction Of Simulation Scenarios: A Systematic Review

Towards An Automatic Construction Of Simulation Scenarios: A...

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2023 Winter Simulation Conference, WSC 2023

作者： Davis, Christopher W.H. Jetter, Antonie J. Giabbanelli, Philippe J. Microsoft 1414 NW Northrup PortlandOR97209 United States Portland State University Dept. of Engineering & Technology Management PortlandOR97201 United States Miami University Dept. of Computer Science & Software Engineering OxfordOH45056 United States

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods. © 2023 IEEE.

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Sustainability of Machine Learning-based Android Malware Detection Using API calls and Permissions 5

Sustainability of Machine Learning-based Android Malware Det...

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Cho, Woosang Lee, Hojun Han, Sangchul Hwang, Youngsup Cho, Seong-Je Dept. of Software Science Dankook University Gyeonggi-do Yongin-si16890 Korea Republic of Dept. of Computer Engineering Konkuk University Chungju Korea Republic of Dept. of Computer Science and Engineering Sun Moon University Asan Korea Republic of Dept. of Sofware Science Dankook University Yongin Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

As the Android platform and malicious apps continue to evolve, most existing Android malware detection techniques using machine learning are turning out to be unsustainable. In this paper, we propose machine learning-based Android malware detection techniques which uses both API calls and permissions as a feature set. These features are complementary and are often used to detect malicious apps. We first analyze whether a 'yearly dataset-based trained classifier' (YDataC) is sustainable or not. The 'yearly dataset-based trained classifier' refers to the classifier that learns from 80% of the dataset of a specific year from 2014 to 2021, and is tested with 20% of the datasets of every year between 2014 and 2021. Through experiments, we discovered that the classification rate has dropped significantly since 2019, and something big has changed. Therefore, the 'yearly dataset-based trained classifier' is judged to be unsustainable. Next, we present and evaluate two incremental learning methods for gradual training: an incrementally trained Random Forest (RF) and an incrementally trained Neural Network (NN). Evaluation results show that two incremental learning classifiers have better sustainability than the 'yearly dataset-based trained classifier'. The incrementally trained RF has better sustainability than the incrementally trained NN in terms of given metrics such as f1 and AUT (Area under Time). © 2022 IEEE.

关键词： Android malware

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models

arXiv

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arXiv 2024年

作者： Kong, Xiangrui Zhang, Wenxiao Hong, Jin Braunl, Thomas Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and solving mathematical problems, leading to advancements in various fields. We propose an LLM-embodied path planning framework for mobile agents, focusing on solving high-level coverage path planning issues and low-level control. Our proposed multilayer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. To evaluate the performance of various LLMs, we propose a coverage-weighted path planning metric to assess the performance of the embodied models. Our experiments show that the proposed framework improves LLMs' spatial inference abilities. We demonstrate that the proposed multi-layer framework significantly enhances the efficiency and accuracy of these tasks by leveraging the natural language understanding and generative capabilities of LLMs. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and claude- 3.5-sonnet. The experimental results show that claude-3.5 can complete the coverage planning task in different scenarios, and its indicators are better than those of the other models. © 2024, CC BY-NC-SA.

关键词： Motion planning

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