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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakaratne, Avishka Pinnawala, Nishith Wijerathna, Kaveesha Senavirathna, Kaushalya Wellalage, Sasini Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Rapid urbanization and increasing income levels combined with poor and insufficient road network to accommodate vehicles is causing a major traffic problem in Sri Lanka. Additionally, in urban areas, traffic congestion is leading to an increase in air and noise pollution as well. Numerous solutions were tried by authorities, yet no promising results were yielded to address these issues successfully. Contrastingly, increasing road network capacity to solve this problem is very costly and feasible only up to a certain point. Another option is to limit the number of vehicles in the city either by law or by alternative means such as ride-sharing. The best ride-sharing method available is the public transportation, however, due to the limitations of it, upper middle-class opt not to use those hence use their own vehicle to get the expected comfortability. This study is aimed at developing a ride-sharing application by profiling the users based on user reputations, vehicle type, socioeconomic variables such as education, social status, and security concerns of the users, and user ratings. Unlike existing carpooling applications that primarily depend on cost and destination to offer ride options, the proposed application further developed to consider the model of the vehicle for fare calculation. © 2021 IEEE.

关键词： Traffic congestion

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Towards a Smart Home : An Intelligent Approach to Environment Monitoring and Anti-Theft Alarming

Towards a Smart Home : An Intelligent Approach to Environmen...

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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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A Telerehabilitation System for the Selection, Evaluation and Remote Management of Therapies

arXiv

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arXiv 2024年

作者： Anton, David Berges, Idoia Bermúdez, Jesús Goñi, Alfredo Illarramendi, Arantza Department of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Department of Languages and Information Systems University of the Basque Country UPV/EHU Donostia-San Sebastián20018 Spain

Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement. © 2024, CC BY.

关键词： Ontology

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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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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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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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Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

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