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An Enhancement Algorithm for Mobility over Fog Computing

An Enhancement Algorithm for Mobility over Fog Computing

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elec.ronic eng.neering (ICEEM), International Conference on

作者： Doaa Abo Hussien Ashraf El-Sisi Nirmeen A. El-Bahnasawy Computer Science & Eng. Dept. Faculty of Electronic Eng. Menoufia University EL-Menoufia Egypt Computer Science Department Faculty of Computers and Information Menoufia University El-Menoufia Egypt

Due to the mobility of users and end devices, providing continuous service in Fog Computing (FC) is a difficult challeng.. With fog-assisted IoT healthcare frameworks, dealing with user mobility becomes even more complex because low latency in mobility support and fog node locating is a critical requirement. In this work, we offer a fog computing architecture with hand-over strategy that is tailored to user mobility and that reduces latency for location-sensitive operations and service stability with great data broadcast quality. In our design, it is no longer necessary to either complete a work or send it to the cloud; instead, it is necessary to connect with other FNs to continue with a request for a new job. This reduces average network delay by 30%-40% and increases end-to-end communication by 35%-45%.

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Particle Swarm Optimization-Based approaches for Cloud-Based Task and Workflow scheduling: A Systematic Literature Review 2

Particle Swarm Optimization-Based approaches for Cloud-Based...

引用

2nd International Conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Singh, Gyan Chaturvedi, Amit K. Govt. Engineering College Dept. of Computer Science and Eng. Ajmer India

ISBN: (纸本)9781665444156

Cloud computing is a novel perspective that provides effective methods to distribute independent and dependent tasks to virtual resources. Task and workflow scheduling play a significant role in optimizing makespan, energy consumption, load balancing, and cost in the cloud environment. Various metaheuristics solutions are proposed to solve scheduling problems. Optimizing the scheduling problem is NP-hard. In this paper, we are presenting an in-dept. review of the current landscape of Particle Swarm Optimization(PSO)-based methods to schedule tasks and workflows. We adopt a systematic literature review(SLR) procedure to selec. studies based on framed Research Questions and predefined inclusion/exclusion criteria from online elec.ronic databases. 39 articles were selec.ed out of 364 articles. Those articles that answer our framed research questions are selec.ed for analysis. The study considers various advantages, drawbacks, properties, simulation and evaluation tools, function objectives of PSO and prepares classification based on PSO modified and PSO hybrid. Finally, a future research direction is presented. © 2021 IEEE.

关键词： Energy utilization

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Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing

Enhancing Facial Recognition in Visual Prostheses using Regi...

引用

International Conference on computer and Applications (ICCA)

作者： Hesham M. Moneer Reham H. Elnabawy Seif Eldawlatly Digital Media Eng. and Technology Dept. German University in Cairo Cairo Egypt Computer and Systems Eng. Dept. Ain Shams University Cairo Egypt Computer Science and Eng. Depr. The American University in Cairo Cairo Egypt

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challeng.ng. This work aims to enhance implantees’ ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses.

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Memory-efficient self-supervised learning of null space projection operators

Memory-efficient self-supervised learning of null space proj...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Zhai, Albert J. Kuo, Joseph Anastasio, Mark A. Villa, Umberto Department of Computer Science University of Illinois Urbana-Champaign IL61801 United States Department of Elec. & Comp. Eng. University of Illinois Urbana-Champaign IL61801 United States Department of Bioengineering University of Illinois Urbana-Champaign IL61801 United States Oden Institute The University of Texas at Austin TX78712 United States

ISBN: (纸本)9781510660311

Many imaging systems can be described by a linear operator that maps object properties to a collection of discrete measurements. The null space of such an imaging operator represents the set of object components that are effectively invisible to the imaging system. The ability to extract the object components that lie within the null space of an imaging operator allows one to analyze and optimize not only the measurement capabilities of the system itself, but also its associated reconstruction methods. An orthogonal null space projection operator (ONPO), which maps any object to its corresponding null space component, offers this ability. However, existing methods for producing an ONPO are limited by high memory requirements. In this work, we develop a novel learning-based method for calculating an ONPO. Numerical results show that our method can produce an accurate ONPO using less memory than existing methods, enabling the characterization of the null spaces of larger imaging operators than previously possible. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Imaging systems

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ECG based heart disease detection using DFT based Convolutional Neural Network

ECG based heart disease detection using DFT based Convolutio...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Rishabh Anand Adyasha Rath Ganapati Panda Dept. of Computer Science & Eng. C. V. Raman Global University Bhubaneswar India Dept. of Electronics & Communication Eng. C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Cardiovascular diseases (CVDs) pose an extreme hazard to human health all around the planet, accounting for a substantial proportion of global mortality. elec.rocardiogram (ECG) signals of human beings is one of the most significant CVD detection procedure because of its non-invasive and cost-effective nature. However, due to their modest amplitudes and short duration, ECG signals, at times, can be difficult to interpret, resulting in heterogeneity in analysis. In the present study, by investigating the use of ML and DL, specifically DL methods, in automated ECG diagnosis to address these problems. There has been pompous work done in the landscape of Neural Network, but very little has been achieved in the domain of the reduction of computational cost. This investigation is aimed to refashion the traditional convolution method into Discrete Fourier Transform (DFT) convolution method in an endeavor to reduce computational complexity and the time taken to perform convolution on ECG signals while proliferating the quality of automated feature detection and extraction. The proposed model achieved high accuracy of 95.81, AUC and F1 Score of 0.9886 and 0.9587 respectively. The study was intended to develop a model that caters to all these needs and extends beyond the scope of our dilemma to aid in accurate HD detection.

关键词： Accuracy Convolution Planets Computational modeling Discrete Fourier transforms elec.rocardiography Computational efficiency Convolutional neural networks Cardiovascular diseases Biological neural networks

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Textual Sentimental Classification using Convolution Neural Network Algorithm

Textual Sentimental Classification using Convolution Neural ...

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2022 International Conference on Computing, Communication and Power Technology, IC3P 2022

作者： Kavitha, K. Chittineni, Suneetha Andhavarpu, Naveen Kumar Chappa, Vasundhara Acharya Nagarjuna University Dept. of Computer Science & Eng. Guntur India Aditya Institute of Technology and Management Department of Information Technology Tekkali India Rvr & Jc College of Engineering Department of Computer Science and Applications Guntur India

ISBN: (纸本)9781665425254

Sentiment analysis on classification of text data is one of the emerging tasks in natural language processing compared to others. In the main, there is a need for big dig for meaningful information from the data present on the internet through sentiment analysis. Deep learning has already been successful in keeping up with sentiment analysis tasks utilizing deep learning models. Nowadays, client happiness is the most crucial factor for every mature firm. As a result, many businesses live and promote their products and services on social media platforms, where they eventually receive evaluations and feedback from their customers. Studying each word and phrase one at a time is time-consuming. Thus, evaluating the sentiment of all texts provides organizations with an overview of how positive and negative users are on a given issue. In this study, we investigated word embedding architecture utilizing the convolutional neural network algorithm or approach (CNN). We created a convolutional neural network architecture using a continuous loop of the tensor flow technique, along with word2vec and evaluated the outcome by using the measures accuracy, precision, recall, and f1-scoring. It reaches an accuracy of more than 92 percent, significantly boosting text categorization accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Reducing Bandwidth and Storage Requirements for Surveillance Videos Using ROI Extraction and Compression

Reducing Bandwidth and Storage Requirements for Surveillance...

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International Conference on Developments in eSystems eng.neering (DESE)

作者： Maryam H. Fadel Ahlam H. Shanin Al-Sudani Sadiq H. Abdulhussain Basheera m. Mahmmod Muntadher Alsabah Abir Hussain Dhiya Al-Jumeily Dept. of Computer Eng. University of Baghdad Baghdad Iraq Medical Technical College Al-Farahidi University Baghdad Iraq Dept. of Electrical Eng. University of Sharjah Sharjah UAE School of Computer Science and Mathematics Liverpool John Moores University Liverpool UK

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

Cloud storage capacity for surveillance videos is restricted by the massive amounts of bandwidth needed to upload them and the substantial storage space they consume. As a result, researchers are actively exploring methods to reduce file sizes while maintaining critical visual details. The goal is to achieve a balance between minimizing the storage size required and preserving video quality, all while keeping costs as low as possible and achieving the best possible results. This paper proposes a developed approach to optimize surveillance video compression. Specifically, each frame, after being acquired, is applied to extract regions of interest (ROI) where motion has occurred, ensuring that the entire desired area is captured. Next, the extracted areas with no changes are zeroed out, reducing unnecessary data. The resulting area is then encoded and transmitted. When the encoded video is received, the decoding process is carried out to restore the video to its original content, i.e., video frames. Three datasets are used to evaluate the performance of the developed algorithm. In addition, the obtained results are compared to the basic video, which shows that the developed algorithm produced good results, outperforming the MJPEG algorithm and existing algorithms.

关键词： Wireless communication Visualization Wireless sensor networks Machine learning algorithms Surveillance Bandwidth Video compression Decoding Data mining Videos

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Graphon-based Synthetic Power System Model and its Application in System Risk Analysis

Graphon-based Synthetic Power System Model and its Applicati...

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IEEE International Smart Cities Conference (ISC2)

作者： Rachad Atat Muhammad Ismail Erchin Serpedin Dept. Electrical and Computer Eng. Texas A&M University at Qatar Doha Qatar Dept. of Computer Science Tennessee Technological University Cookeville TN USA Dept. of Electrical and Computer Eng. Texas A&M University College Station TX USA

The high confidentiality level of power system's data has motivated the ongoing research on the generation of synthetic power grids that mimic actual systems. The existing synthetic models rely on specific geographical and parametric assumptions, which leads to non-generalizable models that overfit the observed data. To fill up this research gap, this paper proposes the use of graphon, a non-parametric graph processing method, to generate graph samples of different sizes with similar topological and elec.rical characteristics as actual power systems. We first estimate the graphon based on realistic parameters of the observed actual power system. Then as an example of a use case, we sample multiple graphs from the graphon in order to provide a general assessment of the power system vulnerabilities.

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Smart tourism chatbot system using Multi-domain Tourism Information DST 14

Smart tourism chatbot system using Multi-domain Tourism Info...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Kang, Hoon-Chul Kang, Ki-Beom Kim, Dong-Hyun Jwa, Myeong-Cheol Ko, Tae-Seung Jwa, Jeong-Woo Jeju Campus of Korea Polytechnic Dept. of Smart Electronic Control Jeju Korea Republic of National Federation of Fisheries Cooperatives Jeju Korea Republic of Jeju National University Dept. of Telecomm. Eng. Jeju Korea Republic of Korea University of Technology and Education School of Computer Science and Eng Cheonan-si Korea Republic of

ISBN: (纸本)9798350335385

The smart tourism service provides tourists with travel planner services and tour guide services for easy and convenient travel throughout the entire travel process. In this paper, we develop the AI-based chatbot service using a pretrained language model (PLM) and provide tourism information so that tourists can make their travel plans. The proposed chatbot system consists of the DST server, the Neo4J graph DB and MySQL DB servers, and the natural language generation (NLG) server. The dialogue state tracking (DST) server understands the intention of tourists' questions to overcome the shortcomings of the previous rule-based chatbot system [7]. We define the domains and slots of the tourism information DST model with the 4W1H method and develop the dataset [12] for transfer learning of the SOM DST model [14]. The Neo4J and MySQL web servers search tourism information from the tourism information knowledgebase and the smart tourism information system, respectively. The NLG server provides the searched tourism information to the smart tourism app. © 2023 IEEE.

关键词： Computational linguistics

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Advanced Solar Power Forecasting: A Hybrid/Ensemble Approach Utilizing Geographic and Meteorological Data 24

Advanced Solar Power Forecasting: A Hybrid/Ensemble Approach...

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Proceedings of the Winter Simulation Conference

作者： Mahdi Darvishi Abdolreza Abhari Fac. of Eng. and Architectural Scie. Toronto Metropolitan University Toronto ON CANADA Dept. of Computer Science Eng . Toronto Metropolitan University Toronto ON CANADA

ISBN: (纸本)9798331534202

Photovoltaic (PV) systems are pivotal in the global energy transition, where accurate solar power forecasting is critical. Traditional forecasting has leaned heavily on solar irradiance data, yet such reliance carries inherent uncertainties and measurement complexities, presenting significant forecasting challeng.s. This paper introduces a novel hybrid/ensemble model that reduces dependence on solar irradiance data, utilizing geographic, meteorological, and temporal data to predict solar power output. Combining the streng.hs of XGBoost and LightGBM algorithms through a linear regression meta-model, our approach demonstrates improved prediction accuracy, evidenced by a mean absolute error (MAE) of 0.033, and an R-squared value of 0.693. This study advances solar power forecasting, enhancing PV system efficiency, and reliability, and promoting sustainable energy investments.

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