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International Conference on Awareness Science and Technology (iCAST)

作者： Ravitha Wathsala Malith Silva Ravindu Kodikara Sadev Ekanayake Narmada Gamage Poojani Gunathilake Department of Computer Systems Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Recently Smart Home concept has been a popular choice as a solution for emerging security related problems. The primary objective of this research was to create a cyber-threat free fully functioning smart home monitoring and anti-theft alarming system with enhanced physical security mechanisms. The focus of this research was to create a holistic and secure smart home system, combining cutting-edge physical security measures. The study introduced novel Intruder Access Prevention methods rooted in human behavior and voice pattern recognition, while also incorporating blockchain and network traffic analysis to safeguard the homeowner's data. Furthermore, a pioneering voice-controlled monitoring mechanism, utilizing protective energy-saving plug technology, was devised to enhance safety within contemporary households. The human behavior recognition and voice recognition-based intruder access prevention system demonstrated over 80% accuracy in intruder prevention, while user data protection mechanism prevents the communication channel from cyber hackings. Further, the smart plug demonstrates reliable and accurate physical environment monitoring with minimum latency. These results underscore the system's significant contribution to home security, marking a noteworthy advancement in the Smart Home concept.

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and Information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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A Patient Specific Algorithm for Plasmodium Malaria Detection on Cell Images

A Patient Specific Algorithm for Plasmodium Malaria Detectio...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Barath Narayanan Narayanan Manawaduge Supun de Silva Russell C. Hardie Sensor and Software Systems Division University of Dayton Research Institute Dayton USA Department of Electrical and Computer Engineering University of Dayton Dayton USA

computer-aided detection of plasmodium malaria on cell images from digital microscopy provides an invaluable second opinion to medical experts. Traditionally, well-established deep learning algorithms are widely used to detect plasmodium, but these techniques have not been deployed due to the uninterpretable nature of the decisions made by the network. To address this, we present an ensemble method that fuses the predictions based on the class activation mapping (CAM) results from multiple networks via a novel selector network. We study the performance on a publicly available dataset of cell images by using three convolutional neural network architectures, Xception, ResNet50, and InceptionV3,to produce CAM results for the selector network and performance comparison. Our proposed approach helps enhance doctors' trust and outperforms the traditional methods by 1%.

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HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets

arXiv

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arXiv 2023年

作者： Syu, Jia-Hao Fojcik, Marcin Lin, Jerry Chun-Wei Cupek, Rafal Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and intelligent diagnosis, and achieve the P4-medicine. However, healthcare data has high sparsity and heterogeneity. In this paper, we propose a Heterogeneous Transferring Prediction System (HTPS). Feature engineering mechanism transforms the dataset into sparse and dense feature matrices, and autoencoders in the embedding networks not only embed features but also transfer knowledge from heterogeneous datasets. Experimental results show that the proposed HTPS outperforms the benchmark systems on various prediction tasks and datasets, and ablation studies present the effectiveness of each designed mechanism. Experimental results demonstrate the negative impact of heterogeneous data on benchmark systems and the high transferability of the proposed HTPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data

Integrity Estimation of Twitter Based Event Recognition Usin...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Siva Rama Lingham N M Parameswari Karthick P T. Subha Rathi Priya G Rajesh J. Jagannathan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India School of Engineering and Technology Jeppiaar University Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events.

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design 32

Reinforcement-Learning Based Method for Accelerating Functio...

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32nd International Conference on computer Theory and Applications, ICCTA 2022

作者： Halim, Youstina M. Ismail, Khaled A. Abd El Ghany, Mohamed A. Ibrahim, Sameh A. Halim, Youssef M. Ain Shams University Faculty of Engineering Mixel Egypt. Electronics and Communications Engineering Dept. Cairo Egypt Germany Electronics Department German University in Cairo Egypt Integrated Electronic Systems Lab Tu Darmstadt Germany Ain Shams University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer Engineering and Software Systems Dept. Cairo Egypt

ISBN: (纸本)9798350320190

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases. © 2022 IEEE.

关键词： Reinforcement learning

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HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets

HTPS: Heterogeneous Transferring Prediction System for Healt...

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Euromicro Conference on Parallel, distributed and Network-Based Processing

作者： Jia-Hao Syu Jerry Chun-Wei Lin Marcin Fojcik Rafał Cupek Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural Networks (GNN) for efficient traffic engineering (TE) in software Defined Networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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ChatGPT, Let Us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

ChatGPT, Let Us Chat Sign Language: Experiments, Architectur...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Nada Shahin Leila Ismail Department of CS and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of IT National Water and Energy Center UAE University Al-Ain UAE CLOUDS lab School of Computing and Information Systems The University of Melbourne Melbourne Australia

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.

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