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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

SN computer Science 2024年第5期5卷 1-11页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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An Enhanced Secure Authentication Scheme for the Internet of Things

An Enhanced Secure Authentication Scheme for the Internet of...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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Sentiment Analysis to manage Health Crisis in Smart Cities 18

Sentiment Analysis to manage Health Crisis in Smart Cities

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Bouhissi, Houda El Itmacene, Ouardia Oubekkou, Milissa Pavlova, Olga Hovorushchenko, Tetiana Shestakevych, Tetiana University of Bejaia LMED Laboratory Faculty of Exact Sciences Bejaia06000 Algeria University of Bejaia Faculty of Exact Sciences Bejaia06000 Algeria Khmelnytskyi National University Department of Computer Engineering & Information Systems Khmelnytskyi Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine

ISBN: (纸本)9798350360462

Sentiment analysis is a branch of natural language processing that analyzes textual data and provides insights into users' opinions. In the context of smart cities, sentiment analysis seeks to seamlessly integrate technological innovations into people's daily lives, fostering sustainability and enhancing overall quality of life while monitoring citizen satisfaction levels. This approach allows for more precise analysis and facilitates the implementation of strategies geared towards improving services and the general quality of life. The aim of this paper is to propose an approach for managing healthcare crises in smart cities. The proposal relies on a hybrid approach that combines LSTM for tweet classification with EHO for optimizing classification parameters. Experimental results have demonstrated the effectiveness of this proposed technique. © 2023 IEEE.

关键词： Smart city

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Augmented Lagrangian Method for Linear Programming Using Smooth Approximation 6th

Augmented Lagrangian Method for Linear Programming Using Sm...

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6th International Conference on Dynamics of Information systems, DIS 2023

作者： Moosaei, Hossein Ketabchi, Saeed Syed, Mujahid N. Bazikar, Fatemeh Department of Informatics Faculty of Science Jan Evangelista Purkyně University Ústí nad Labem Czech Republic Department of Applied Mathematics Faculty of Mathematical Sciences University of Guilan Rasht Iran Department of Industrial and Systems Engineering Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals KSA Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran

ISBN: (纸本)9783031503191

The augmented Lagrangian method can be used for finding the least 2 - norm solution of a linear programming problem. This approach’s primary advantage is that it leads to the minimization of an unconstrained problem with a piecewise quadratic, convex, and differentiable objective function. However, this function lacks an ordinary Hessian, which precludes the use of a fast Newton method. In this paper, we apply the smoothing techniques and solve an unconstrained smooth reformulation of this problem using a fast Newton method. Computational results and comparisons are illustrated through multiple numerical examples to show the effectiveness of the proposed algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Newton-Raphson method

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Shedding Light on Glowapp: A Usability Test Evaluation for Enhanced User Experience 9

Shedding Light on Glowapp: A Usability Test Evaluation for E...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sergio Saputro, Immanuela Puspasari Prabowo, Yulius Denny Ticoalu, Voice Esther Mahardini, Karunia Agung BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Cosmetics is one of the emerging industries in Indonesia. Cosmetology is the practice of using cosmetics and cosmetic products to alter the appearance of the face. When applying makeup, it is essential to tailor it to the occasion and consider the facial structure. Applying appropriate cosmetics may enhance self-assurance. According to the questionnaire findings, women apply makeup more than twice. However, not all of them possess the talent to apply makeup on themselves. They struggle to locate a makeup artist with a style and availability that meets their requirements. Glowapp was developed with booking functionalities. Customers will find it more convenient to schedule an appointment. Blackbox testing was utilized to assess the functionality of the system. The results indicate that the features performed effectively and satisfied user requirements. The usability test is conducted by integrating the eight golden guidelines and ISO-9241-11. The goal is to reduce the time respondents spend filling questionnaires while maintaining usability principles to ensure more accurate answers. Glowapp has a performance score of 93%, with efficiency scoring the highest and effectiveness scoring the lowest. © 2024 IEEE.

关键词： Black-box testing

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Maximizing the Utilization of Demand Response in Multi-Microgrids: A Game-Theoretic Approach

Maximizing the Utilization of Demand Response in Multi-Micro...

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IEEE General Meeting Power& Energy Society

作者： Soheil Mohseni Alan C. Brent Sustainable Energy Systems School of Engineering and Computer Science Wellington Faculty of Engineering Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The existing body of literature pertaining to incentive-based demand-side management within microgrids, driven by market dynamics, has predominantly concentrated on the minimization of operational expenses. This focus, however, has omitted a comprehensive analysis of the competitive dynamics inherent in decentralized energy markets. As a result, discernible indications of sub-optimal performance in practice has surfaced. To address this gap, this study employs principles rooted in non-cooperative game theory to elucidate behavioral uncertainties. The present paper introduces an anticipatory, aggregator-mediated framework for demand response scheduling, fully integrated into an optimal dispatching problem governing interconnected multi-microgrids. Through application to an illustrative multi-microgrid system, the outcomes underscore the efficacy of this proposed model in achieving an optimal equilibrium between internal demand response utilization and electricity importation. Notably, the model addresses the epistemological uncertainties stemming from an incomplete understanding of behavioral patterns exhibited by key stakeholders – utilities, load aggregators, and end-consumers. Numerical findings highlight the potential of the model to curtail the daily operational costs of the case study system by a24%.

关键词： Electric potential Uncertainty Electricity Microgrids Minimization Demand response Numerical models Stakeholders Game theory Load modeling

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Line Fitting-Based Corner-Like Detector for 2D Laser Scanners Data∗ 10

Line Fitting-Based Corner-Like Detector for 2D Laser Scanner...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Sousa, Ricardo B. Sobreira, Heber Miguel Silva, Manuel F. Moreira, Antonio Paulo University of Porto Faculty of Engineering Porto4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal ISEP Polytechnic of Porto R. Dr. António Bernardinode Almeida Porto4249-015 Portugal

ISBN: (纸本)9798350394245

The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates. © 2024 IEEE.

关键词： Feature extraction

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Geometric Modelling of Fractal Structured Objects based on Integration of R-Function And L-System Methods

Geometric Modelling of Fractal Structured Objects based on I...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Azimbayevna, Shakhlo Sadullaeva Berdiev, Golib Ogli, Adhamjon Toxtasinov Ilhomjon Ilxom Kizi, Maftuna Ismoilova Department of Convergence of digital technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Uzbekistan Department of Computer Systems Karshi branch of the Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Uzbekistan

ISBN: (纸本)9798400716928

The article offers suggestions about mathematical modeling of architectural objects based on the functional image using the constructive operations of the theory of R-functions. Given in 2D format 3D surface from the data equations work exit according to approach offer done. Work developed mathematician from the model using construction - architecture L-systems in objects based on three measured geometric to the object processing to give through fractal 3D computer design of an architectural building model work developed. In the article work developed samples of the method opportunities reflection made. Fractal geometry terminology and concepts using natural surfaces, forms enough level right, good quality and quantitative way described. In architecture forms structure spatial organize to be done fractal geometry using natural structures like enough level description possible has been repetitive to himself similar from structures use, fractal construction the rules mathematician models and computer graphics using in architecture application this direction possibilities efficiency increases. © 2024 ACM.

关键词： Fractals

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RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection

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Chinese Journal of Electronics 2020年第6期29卷 1093-1101页

作者： LYU Yanxia LI Wenjie WANG Yue SUN Siqi WANG Cuirong School of Computer Science and Engineering Northeastern University Information Systems Faculty of EngineeringThe University of Melbourne

Anomaly detection refers to identify the true anomalies from a given data *** present an ensemble anomaly detection method called Relative mass and half-space tree based forest(RMHSForest),which detect anomalies,including global and local anomalies,based on relative mass estimation and halfspace *** from density or distance based measure,RMHSForest utilizes a novel relative mass estimation to improve the detection of local ***,half-space tree based on augmented mass can estimate a mass distribution efficiently without density or distance calculations or *** empirical results show that RMHSForest outperforms the current popular anomaly detection algorithms in terms of AUC and processing time in the test data sets.

关键词： pattern clustering security of data trees (mathematics) AUC data set relative mass estimation popular anomaly detection algorithms mass distribution augmented mass local anomaly halfspace tree local anomalies global anomalies ensemble anomaly detection method half-space tree RMHSForest Relative mass Half-space tree Anomaly detection Ensemble methods

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