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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Klaib, Alhadi A. Talooh, Mohamed Ahmed M. Arbi, Abdelrahman Elmergib University Department of Software Engineering Khoms Libya Higher Institute of Science and Technology Department of Computer Science Khoms Libya Higher Colleges of Technology Department of Electrical Engineering Ras Al-Khaimah United Arab Emirates

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The spread of COVID-19 causes a threat to the whole world. It has affected most human life activities during the last two years. The higher education sector was among the industries massively affected by this pandemic, all educational institutions in Libya were locked down, and a large number of students were left without education. This paper investigates the implementation of E-learning in the Higher Institute of science and Technology - Khoms in Libya to determine the challenges and limitations the instructors and the institute have faced in implementing E-learning. Thus, a questionnaire was designed and distributed among the instructors in the Institute. This paper discusses several elements of E-learning, including the extent of using E-learning, E-learning skills, benefits of E-learning, and obstacles faced in implementing E-learning. The obtained results show that the IT infrastructure in the Institute must be upgraded, and stable high-speed internet access is required for a smooth implementation of E-learning. © 2022 IEEE.

关键词： COVID-19

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A novel privacy preserving data aggregation scheme with data integrity and fault tolerance for smart grid communications

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Frontiers of computer science 2021年第5期15卷 217-219页

作者： Haiyong BAO Beibei LI Software Engineering Institute East China Normal UniversityShanghai 200062China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China College of Cybersecurity Sichuan UniversityChengdu 610065China

1 Introduction By making the best of the information technology in smart grid,considerable power energy can be effectively saved[1,2].However,frequently collecting userJs power consumption data incurs privacy disclosure ***,data integrity is also critical to make the decisions to be more credible and *** the smart meters for collecting electricity consumptions are prone to communication failures for being deployed in unattended environment commonly.

关键词： smart preserving fault

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

Probabilistic Graph Modeling based Safety Classifier Algorit...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Karkouri, Najib El Tigani, Smail Saadane, Rachid Chehri, Abdelah Pierre, Samuel Neya, Noureddine Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Accident prevention

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Forecasting the Stability of COVID-19 Vaccine Companies Stock Market using LSTM and Time-series Models

Forecasting the Stability of COVID-19 Vaccine Companies Stoc...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Rahemeen Khan Safia Baloch Department of Software Engineering Bahria University Karachi Pakistan Department of Computer Science FAST University Karachi Pakistan

Stock price forecasting is challenging because stock markets are volatile and nonlinear in nature. With the impact of the Covid-19 outbreak in 2019, stock market forecasting has become more complicated. The goal of this study is to identify the top brands in the global covid-19 vaccination market and their impact on the stock market. By forecasting the next months stock exchange, traders will be able to select the most suitable company to invest in. In this paper, MSE, RMSE, and MAPE are used as evaluation matrices to compare timeseries and neural network models such as Arima, Prophet, and LSTM. According to the results of a comparative analysis, the LSTM model produced the most accurate predictions on majority stock prices dataset for the selected vaccination brands. Brand stability is achieved by predicting future stock exchange prices using an outperformed LSTM model. As a result, BioNTech has been identified as a high potential stable brand that the buyer can invest to gain enormous returns among the massive number of probabilistic events.

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Deep Learning Approaches for Protein Secondary Structure Prediction

Deep Learning Approaches for Protein Secondary Structure Pre...

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International Conference on computer and Information Technology (ICCIT)

作者： Umme Israt Afroz Shreshthajit Das Nazmul Siddique Department of Computer Science and Engineering Khulna University of Engineering & Technology(KUET) Khulna Bangladesh Department of Software Engineering Shahjalal University of Science & Technology(SUST) Sylhet Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry UK

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper presents a novel hybrid model comprising Evolutionary Scale Modeling (ESM), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) network for prediction of protein secondary structures from coil (C), helix (H), and sheet (E)—from amino acid sequences using deep learning techniques. Each architecture leverages unique strengths, with LSTMs capturing long-range dependencies, CNNs extracting local spatial patterns, and ESM enhancing contextual understanding of sequences. The hybrid model was trained and tested using two key datasets: the UniProt dataset and the pdb-intersect-pisces dataset, which provide a rich source of protein sequences and structural information. The proposed model achieved an accuracy of 89.22%, demonstrating robust performance in protein secondary structure prediction.

关键词： Proteins Deep learning Accuracy Computational modeling Neural networks computer architecture Predictive models Transformers Information technology Long short term memory

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RemoteBlock: A Scalable Blockchain Storage Framework for Ethereum

RemoteBlock: A Scalable Blockchain Storage Framework for Eth...

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Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Ding Xia Peiyuan Yao Junyuan Liang Wuhui Chen School of Computer Science Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

This paper addresses the continuously increasing storage demands challenge faced by blockchain networks, with a particular focus on Ethereum. We propose a novel framework that divides the network into consensus nodes, which inherit Ethereum characteristics, and storage nodes responsible for storing Merkle Patricia Trie (MPT) nodes. This design aims to reduce the storage load on individual nodes by distributing MPT nodes based on their key values. Our approach maintains network security and data integrity while easing the storage burden through a distributed storage mechanism. Key to our work is the dynamic adjustment of storage load across an expandable network of storage nodes. We validate our framework through practical experiments, involving modifications to the go-ethereum source code and testing with authentic Ethereum block data. The results confirm that our work not only mitigates storage issues but also enhances synchronization efficiency.

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Spark Performance Optimization Analysis with Multi-Layer Parameter using Shuffling and Scheduling in Different Data Caching Options 16

Spark Performance Optimization Analysis with Multi-Layer Par...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Adinew, Deleli Mesay Shijie, Zhou Alemu, Ayall Tewodros University of Electronic Science and Technology of China Department of Software Engineering China Dilla University Department of Computer Science Ethiopia

ISBN: (纸本)9781665405539

As social networking services and e-commerce are growing rapidly, the number of online users also dynamically growing which facilitates the contribution of huge content to the digital world. In such a dynamic environment, meeting the demand of computing is very challenging special with the existing computing model. Although Spark is recently introduced to alleviate the problems with the concept of in-memory computing for big data analytic with many parameters configuration that allow to conFigure and improve its performance, still it has a performance bottleneck which requires to investigate peformance improvement mechanism by focusing on the combinations of Scheduling and Shuffle Manager with intermediate data caching options. The standalone cluster computing model was selected as an experimental methodology with submit command line for data submission. Three Spark applications such as WorkCount, TeraSort, and PageRank were selected and developed for the *** a result, 2.45% and 8.01% performance improvement are achieved in OFFHEAP and Memory Only Ser data caching option, respectively. © 2021 IEEE.

关键词： Data Analytics

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Recursive Broadcasting Approach

Recursive Broadcasting Approach

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Hovhannes A. Harutyunyan Narek Hovhannisyan Department of Computer Science and Software Engineering Concordia University Montreal QC Canada NAS RA Institute for Informatics and Automation Problems Yerevan Armenia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce.

关键词： Multicast algorithms Network topology Multiprocessor interconnection Heuristic algorithms Broadcasting Approximation algorithms Polynomials Topology

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View Transformation Robustness for Multi-View 3D Object Reconstruction with Reconstruction Error-Guided View Selection 39

View Transformation Robustness for Multi-View 3D Object Reco...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Qi Luo, Zhouhang Yu, Tao Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China

ISBN: (纸本)157735897X

View transformation robustness (VTR) is critical for deep-learning-based multi-view 3D object reconstruction models, which indicates the methods' stability under inputs with various view transformations. However, existing research seldom focuses on view transformation robustness in multi-view 3D object reconstruction. One direct way to improve the models' VTR is to produce data with more view transformations and add them to model training. Recent progress on large vision models, particularly Stable Diffusion models, has provided great potential for generating 3D models or synthesizing novel view images with only a single image input. To fully utilize the power of Stable Diffusion models without causing extra inference computation burdens, we propose to generate novel views with Stable Diffusion models for better view transformation robustness. Instead of synthesizing random views, we propose a reconstruction error-guided view selection method, which considers the reconstruction errors' spatial distribution of the 3D predictions and chooses the views that could cover the reconstruction errors as much as possible. The methods are trained and tested on sets with large view transformations to validate the 3D reconstruction models' robustness to view transformations. Extensive experiments demonstrate that the proposed method can outperform state-of-the-art 3D reconstruction methods and other view transformation robustness comparison methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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