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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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Machine Learning (ML) assisted Edge security framework on FPGAs

Machine Learning (ML) assisted Edge security framework on FP...

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international conference on Computer and Communication Engineering, ICCCE

作者： Abdul Manan Sheikh Md. Rafiqul Islam Mohamed Hadi Habaebi Suriza Ahmad Zabidi Athaur Rahman Bin Najeeb Ahmed Basahel Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

Edge computing (EC) is an act of bringing computational and storage capability near data sources. It helps to reduce response times and bandwidth requirements. However, the rapid proliferation of edge devices has expanded the attack surface and opportunity for adversaries to penetrate corporate networks. The limited computational abilities of edge devices and the heterogeneous nature of communication protocols further increase the security challenges of EC. Also, the trustworthiness of hardware devices is challenged due to security and privacy threats like trojan insertion, IP cloning, and hardware counterfeits. The application of Machine Language (ML) models in the edge computing paradigm creates a distributed intelligence architecture. Also, Field Programmable Gate Arrays (FPGAs) can exploit Physical Unclonable Functions (PUFs) characteristics to generate and store authentication keys. The PUF structure deployed with ML models in the edge layer can learn its complex input-output mapping from the Challenge and Response pairs (CRPs) to identify the suspicious and unknown responses. This article discusses the security and privacy issues in various layers of the EC architecture and proposes intrusion detection systems through the integration of FPGA-based edge sever and ML models. A PUF-assisted ML framework of the intrusion detection system is proposed to authenticate and detect potential attacks on the network.

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DCTCADM: data-Centric cognitive Agent Decision Making Based an Effective Handoff for Heterogeneous Wireless Communication

DCTCADM: Data-Centric Cognitive Agent Decision Making Based ...

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international conference on Recent Trends in Advance computing (ICRTAC)

作者： S. Muthulingam V. Pandiyaraju K Malar U. Samson Ebenezar N Manjunathan Goutham Elumalai Computer Science and Engineering Alliance University Bengaluru India Computer Science and Engineering Vellore Unstitute of Technology Chennai India Computer Science and Engineering MRIET Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical And Technical Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Sci and Technology Chennai India

An effective handover decision-making methodology mainly concentrates on minimal handover drop and quick handoff process between two various networks. DCCAD algorithm combination of the cognitive Congestion Control Artificial Immune System (CoCAI), signal and velocity threshold algorithm (SVT), and Optimized Smart Communication Card (SCom Card) technique. DCCAD and the CoCAI approach aim to enhance the efficiency of network selection and maintain high call connectivity rates to facilitate seamless communication, even for mobile users navigating challenging mobility management systems within diverse wireless networks. The CoCAI method is responsible for overseeing and managing the selection of optimal networks while keeping connected mobile nodes connected. Additionally, this method monitors unnecessary handovers, considering user acceptance and the Received Signal Strength (RSS) level. Both DCCAD and CoCAI analyze the velocity and direction of mobile nodes by leveraging sequential Received Signal Strength Signal (SRSS) measurements. Moreover, CoCAI acquires details about the nearest networks using a nearest neighboring concept with the SVT ratio algorithm. An optimization technique, referred to as AHO, dynamically adjusts the threshold levels for node speed, load, and quality expectations based on received signal strength and user preferences. SCom Card provides a unique ID while starting communication. Though the ID master station has all the information about the sender all networks will perform the handover with a unique SCom Card ID only no other factors are required for the handover process. Due to this Card will reduce the handover time maximum. DCCAD method provides good results with all types of handovers. Especially for vertical hard handover and vertical soft handover.

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Robotic Packaging Optimization with Reinforcement Learning

Robotic Packaging Optimization with Reinforcement Learning

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Eveline Drijver Rodrigo Pérez-Dattari Jens Kober Cosimo Della Santina Zlatan Ajanović Cognitive Robotics Department Delft University of Technology Delft CD The Netherlands

Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work investigates automated secondary robotic food packaging solutions that transfer food products from the conveyor belt into containers. A major problem in these solutions is varying product supply which can cause drastic productivity drops. Conventional rule-based approaches, used to address this issue, are often inadequate, leading to violation of the industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning responsive and predictive policy, based on experience. However, it is challenging to utilize it in highly complex control schemes. In this paper, we propose a reinforcement learning framework, designed to optimize the conveyor belt speed while minimizing interference with the rest of the control system. When tested on real-world data, the framework exceeds the performance requirements (99.8% packed products) and maintains quality (100% filled boxes). Compared to the existing solution, our proposed framework improves productivity, has smoother control, and reduces computation time.

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Construction and analysis of Tibetan Khampa dialect corpus for speech synthesis

Construction and analysis of Tibetan Khampa dialect corpus f...

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Oriental COCOSDA international conference on Speech database and Assessments

作者： Yi Zhu Wenhuan Lu Yangzom Mengfei Hu Kuntharrgyal Khysru Jianguo We Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu University Xining China

High-quality corpus is the basis for supporting speech synthesis research, and the quality and efficiency of speech synthesis are directly affected by a good or bad speech synthesis corpus. The limited scope of Tibetan language usage and the complexity of phonology and grammar make the resources for this language relatively scarce, leading to a lag in the development of related technology applications. To address this situation, we designed an filtering algorithm based on phoneme balanced Tibetan text data, and selected 9179 Tibetan utterances from a large-scale text database as the text corpus to be used in building the corpus; then, we selected a professional male presenter of the Tibetan Khampa dialect to record the text corpus and build a speech synthesis corpus of about 25 hours in the Tibetan Khampa dialect. The statistical analysis and experimental results show that the synthesis model trained with this corpus can obtain natural and comprehensible speech, which verifies the completeness, validity and usefulness of this corpus.

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What Affects the Adoption of Metaverse in Education? A SEM-based Approach

What Affects the Adoption of Metaverse in Education? A SEM-b...

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international Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Rohani Rohan Pranab Roy Vajirasak Vanijja Suree Funilkul Subhodeep Mukherjee Debajyoti Pal School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Computer Science and Engineering JK Laxmipat University Jaipur India Department of Operations Management GITAM (Deemed to be University) Visakhapatnam India Innovative Cognitive Computing Research Center KMUTT Bangkok Thailand

Metaverse is an emerging 3D digital space that augments the real world and the virtual world. It has been envisioned to be the trendsetter for future education having tremendous potential. However, the success of any new technology depends on its mass adoption and widespread societal diffusion. Therefore, it becomes important to understand the factors that can lead to its success by ensuring widespread adoption. In this work, we investigate the different psychological gratifications of the educational usage of Metaverse under the lens of the Uses and Gratification (UGT) theory. Particularly, we propose and test a research model by using the Structural Equation Modelling (SEM) approach by considering four fundamental psychological gratifications of autonomy, achievement, affiliation, and dominance, together with two additional factors of hedonic motivation and student personality. data is collected from two Asian universities, and results show the importance of autonomy, hedonic motivation, and personality in predicting Metaverse adoption.

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Efficient Multi-Task Scene Analysis with RGB-D Transformers

Efficient Multi-Task Scene Analysis with RGB-D Transformers

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international Joint conference on Neural Networks (IJCNN)

作者： Söhnke Benedikt Fischedick Daniel Seichter Robin Schmidt Leonard Rabes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany

Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving these tasks given limited computing and battery capabilities on mobile platforms is challenging. To address this challenge, we introduce an efficient multi-task scene analysis approach, called EMSAFormer, that uses an RGB-D Transformer-based encoder to simultaneously perform the aforementioned tasks. Our approach builds upon the previously published EMSANet. However, we show that the dual CNN-based encoder of EMSANet can be replaced with a single Transformer-based encoder. To achieve this, we investigate how information from both RGB and depth data can be effectively incorporated in a single encoder. To accelerate inference on robotic hardware, we provide a custom NVIDIA TensorRT extension enabling highly optimization for our EMSAFormer approach. Through extensive experiments on the commonly used indoor datasets NYUv2, SUNRGB-D, and ScanNet, we show that our approach achieves state-of-the-art performance while still enabling inference with up to 39.1 FPS on an NVIDIA Jetson AGX Orin 32 GB.

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On Scalable and Interpretable Autism Detection from Social Interaction Behavior

On Scalable and Interpretable Autism Detection from Social I...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： William Saakyan Matthias Norden Lola Herrmann Simon Kirsch Muyu Lin Simon Guendelman Isabel Dziobek Hanna Drimalla Center for Cognitive Interaction Technology (CITEC) Bielefeld University Germany Institute of Psychology Humboldt University of Berlin Department of Psychiatry and Psychotherapy Medical Center-University of Freiburg

Autism Spectrum Condition (ASC) is characterized by social interaction difficulties that can be challenging to assess objectively in the diagnostic process. In this paper, we evaluate the capability of using videos of a standardized social interaction to differentiate non-verbal behaviors of individuals with and without ASC. We collected a large video dataset consisting of 164 participants with ASC (n = 83) and neurotypical individuals (n = 81) who completed the computer-based Simulated Interaction Task (SIT) in different studies including lab and home settings. To classify individuals with and without ASC, we trained uni-and multimodal machine learning models based on different modalities such as facial expressions, gaze behavior, head pose and voice features. Our results indicate that a multimodal late fusion approach achieved the highest accuracy (74%). In the unimodal setting, classification based on facial expressions (accuracy 73%) and voice features (accuracy 70%) were most effective. An explainability analysis of the most relevant features for the facial expression model indicated that features from all emotional parts as well as from both the speaking and listening part of the interaction were informative. Based on our results, we developed a scalable online version of the SIT to collect diverse data on a large scale for the development of machine learning models that can differentiate between different clinical conditions. Our study highlights the potential of machine learning on videos of standardized social interactions in supporting clinical diagnosis and the objective and effective measurement of differences in social interaction behavior.

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Generating Scenarios from High-Level Specifications for Object Rearrangement Tasks

Generating Scenarios from High-Level Specifications for Obje...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Sanne van Waveren Christian Pek Iolanda Leite Jana Tumova Danica Kragic School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Department of Cognitive Robotics Delft University of Technology Delft the Netherlands Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

Rearranging objects is an essential skill for robots. To quickly teach robots new rearrangements tasks, we would like to generate training scenarios from high-level specifications that define the relative placement of objects for the task at hand. Ideally, to guide the robot's learning we also want to be able to rank these scenarios according to their difficulty. Prior work has shown how generating diverse scenario from specifications and providing the robot with easy-to-difficult samples can improve the learning. Yet, existing scenario generation methods typically cannot generate diverse scenarios while controlling their difficulty. We address this challenge by conditioning generative models on spatial logic specifications to generate spatially-structured scenarios that meet the specification and desired difficulty level. Our experiments showed that generative models are more effective and data-efficient than rejection sam-pling and that the spatially-structured scenarios can drastically improve training of downstream tasks by orders of magnitude.

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Adaptive Neuro-Fuzzy Inference System-based Lightweight Intrusion Detection System for UAVs

Adaptive Neuro-Fuzzy Inference System-based Lightweight Intr...

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conference on Local Computer Networks (LCN)

作者： Alvi Ataur Khalil Mohammad Ashiqur Rahman Electrical and Computer Engineering Florida International University USA Knight Foundation School of Computing and Information Sciences Florida International University USA

Unmanned aerial vehicles (UAVs) are widely utilized in myriad domains due to their low infrastructure cost and flexibility in deployment. Hostile and unsafe networking environments can make UAVs vulnerable to various attacks. Intrusion detection systems (IDSs) have been developed to detect such attacks. However, conventional data-driven IDSs can be architecturally complex and computationally intensive for resource-constrained small UAVs. In this work, we propose a lightweight IDS for UAVs leveraging an adaptive neuro-fuzzy inference system (ANFIS) that combines artificial neural networks (ANNs) and fuzzy deduction frameworks. Due to the simplistic membership and rule-based classification capabilities of ANFIS, our proposed IDS is lightweight and perfectly suitable for small UAVs. We evaluate the ANFIS-IDS’s effectiveness by comparing its performance to conventional data-driven classification models. In particular, we contrast the proposed IDS with a traditional novelty-based IDS for UAV sensor attacks. We further compare their deployment in a hardware-emulated UAV testbed, assessing the proposed model’s lightweight nature.

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