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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Hybrid Smart Contracts for Securing IoMT Data

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computer Systems science & engineering 2023年第1期44卷 457-469页

作者： D.Palanikkumar Adel Fahad Alrasheedi P.Parthasarathi S.S.Askar Mohamed Abouhawwash Department of Computer Science and Engineering Dr NGP Institute of TechnologyCoimbatore641048India Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathy638401India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Data management becomes essential component of patient *** of Medical Things(IoMT)performs a wireless communication between E-medical applications and human *** of consulting a doctor in the hospital,patients get health related information remotely from the *** main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care *** overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured *** health data is collected from physician,smart devices *** main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective *** this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.

关键词： PoW byzantine fault tolerance IoMT cloud computing health care data

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Access Management in IoT: Implementing a Secure Access Control System 15

Access Management in IoT: Implementing a Secure Access Contr...

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15th International Conference on Communications, COMM 2024

作者： Neculai, Razvan-Sebastian Bogdan, Ioana-Corina Alexandru, Marian Robu, Dan-Nicolae Transilvania University of Brasov & Eviden Faculty of Electrical Engineering and Computer Science Department of Cybersecurity Braşov Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Braşov Romania

ISBN: (纸本)9798350355956

This paper aims to implement and use the Spring Security framework to secure and authenticate the connection between a web application and the ESP32 device. In the context of a web application, by analyzing the integration of the Spring Security framework, it investigates the ways in which it manages the complex process of authentication and authorization, thus providing a secure and protected connection to an IoT device. Additionally, this study investigates the integration and use of the RTOS operating system to manage physical access in an IoT environment, in particular by implementing the opening of a door using tags. The mechanism described above leads to an additional level of security and control over physical access in an IoT ecosystem. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Sizing electrolyzer capacity in conjunction with an off-grid photovoltaic system for the highest hydrogen production

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Energy Harvesting and Systems 2023年第2期10卷 331-348页

作者： Hassan, Qusay Abbas, Majid K Tabar, Vahid Sohrabi Tohidi, Sajjad Abdulrahman, Imad Saeed Salman, Hayder M. Department of Mechanical Engineering University of Diyala Baqubah Iraq Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq

The electrolysis of renewable energy to produce hydrogen has become a strategy for supporting a decarbonized economy. However, it is typically not cost-effective compared to conventional carbon-emitting methods. Due to the predicted intermediate of low-and zero-marginal-cost renewable energy sources, the ability of electrolysis to connect with electricity pricing offers a novel way to cost reduction. Moreover, renewables, particularly photovoltaics, have a deflationary effect on the value of the grid when they are deployed. This study investigates solar electrolysis configurations employing photovoltaic cells to feed a proton exchange membrane water electrolyzer for hydrogen production. Using experimental meteorological data at 1-min precision, the system has been evaluated in Baghdad, the capital of Iraq. Positioned at the yearly optimum tilt angle for the selected site, the solar array is rated at 12 kWp. Temperature effects on solar module energy loss are taken into account. Several electrolyzers with capacities ranging from 2 to 14 kW in terms of hydrogen production were examined to determine the efficacy and efficiency of renewable sources. MATLAB was utilized for the simulation procedure, with a 2021-2035 project lifespan in mind. The results suggest that a variety of potentially cost-competitive options exist for systems with market configurations that closely approximate wholesale renewable hydrogen. At 4313 h of operation per year, the planned photovoltaic array generated 18,892 kWh of energy. The achieved hydrogen production cost ranges between & amp;dollar;5.39/kg and & amp;dollar;3.23/kg, with an ideal electrolyzer capacity of 8 kW matching a 12 kWp photovoltaic array capable of producing 450 kg/year of hydrogen at a cost of & amp;dollar;3.23/kg. & amp;copy;2022 Walter de Gruyter GmbH, Berlin/Boston. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Electrolysis

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An efficient computational procedure to solve the biological nonlinear Leptospirosis model using the genetic algorithms

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Soft Computing 2024年第1期28卷 607-625页

作者： Sabir, Zulqurnain Ali, Mohamed R. Raja, Muhammad Asif Zahoor Sadat, R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Banha Egypt Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD) model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e., ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms of disease spread. The optimization of an error-based fitness function, which is constructed through the differential mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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A Lightweight Multidendritic Pyramidal Neuron Model With Neural Plasticity on Image Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4415-4427页

作者： Zhang, Yu Cai, Pengxing Sun, Yanan Zhang, Zhiming Lei, Zhenyu Gao, Shangce University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan Sichuan University Department of Artificial Intelligence College of Computer Science Chengdu610065 China

Simulating the method of neurons in the human brain that process signals is crucial for constructing a neural network with biological interpretability. However, existing deep neural networks simplify the function of a single neuron without considering dendritic plasticity. In this article, we present a multidendrite pyramidal neuron model (MDPN) for image classification, which mimics the multilevel dendritic structure of a nerve cell. Unlike the traditional feedforward network model, MDPN discards premature linear summation integration and employs a nonlinear dendritic computation such that improving the neuroplasticity. To model a lightweight and effective classification system, we emphasized the importance of single neuron and redefined the function of each subcomponent. Experimental results verify the effectiveness and robustness of our proposed MDPN in classifying 16 standardized image datasets with different characteristics. Compared to other state-of-the-art and well-known networks, MDPN is superior in terms of classifica-tion accuracy. © 2020 IEEE.

关键词： computer architecture

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A Compositional Simulation Framework for Testing Adversarial Robustness of Deep Neural Networks

A Compositional Simulation Framework for Testing Adversarial...

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2023 Design and Verification Conference and Exhibition Europe, DVCon Europe 2023

作者： Nader, Youssef Maher Hatab, Mostafa Lotfy Ghaleb, Mazen Mostafa Bakr, Safia Medhat Awaad, Tasneem A. AlGanzouri, Ahmed Abdelsalam, Mohamed El-Kharashi, M. Watheq The Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Department of Electrical and Computer Engineering Faculty of Engineering & Computer Science University of Victoria Victoria Canada Siemens EDA Cairo Egypt

ISBN: (纸本)9783800762064

Deep neural networks (DNN) have reached impressive performance in computer vision, making them a natural choice for object detection problems in automated driving. However, DNNs used for object detection are known to be highly vulnerable to adversarial attacks. Even small changes to the input such as adding a customized noise pattern that remains invisible to the human eye, can stimulate silent prediction errors. In this study, we present a compositional simulation framework for testing the adversarial robustness of DNNs used in object detection. We demonstrate our framework with a comprehensive case study of a speed-sign detector model with two different adversarial attacks. © 2023 Design and Verification Conference and Exhibition Europe, DVCon Europe

关键词： Object detection

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Improved Bounds on the Size of Permutation Codes Under Kendall τ-Metric

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IEEE Transactions on Information Theory 2025年第6期71卷 4156-4166页

作者： Parvaresh, Farzad Sobhani, Reza Abdollahi, Alireza Bagherian, Javad Jafari, Fatemeh Khatami, Maryam University of Isfahan Faculty of Engineering Department of Electrical Engineering Isfahan81746-73441 Iran School of Mathematics Tehran19395-5746 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan81746-73441 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Pure Mathematics Isfahan81746-73441 Iran

In order to overcome the challenges caused by flash memories and also to protect against errors related to reading information stored in DNA molecules in the shotgun sequencing method, the rank modulation method has been proposed. In the rank modulation framework, codewords are permutations. In this paper, we study the largest size P(n, d) of permutation codes of length n, i.e., subsets of the set Sn of all permutations on {1, . . . , n} with the minimum distance at least d ∈ {1, . . . ,(Formula presented)} under the Kendall τ-metric. By presenting an algorithm and two theorems, we improve the known lower and upper bounds for P(n,d) . In particular, we show that P(n,d)=4 for all n ≥ 6 and (Formula presented) (Formula presented) © 1963-2012 IEEE.

关键词： Flash memory

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