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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Patel, Margi Joshi, Brijendra Kumar Indore Institute of Science and Technology Department of Computer Science and Engineering Indore India Military College of Telecommunication Engineering Department of Informatics Mhow India

ISBN: (纸本)9781665480383

The computer has seen significant evolution in recent years, with applications in variety of disciplines like Machine Learning, Deep Learning etc. Machine Translation (MT) technology has advanced significantly as a subfield, with many methodologies and techniques. The number of individuals using the internet has risen tremendously. Most documents are written in English since that is the most extensively used language on the internet. If a user's first dialect is Gujarati, he or she will naturally prefer to access the information in Gujarati whenever possible. Even though there are already various MT systems and tools that support Indian languages;however, the translation's quality is mediocre and might be improved. As observed, when models are trained on limited quantities of parallel data, their performance declines. Learned models often have limited performance (inaccurate translations and feature scores) along with low coverage (high out-of-vocabulary rates). Furthermore, the researchers were driven to present novel methodologies and solutions that would automatically construct Datasets for MT due to the increased demand for effective technologies to process and translate information from/to Gujarati Language. Our objective of generating a Gujarati-English dataset has been met in two ways. We have already introduced GEDset, that is automatic Dataset Builder for Machine Translation System with Specific Reference to Gujarati-English [1]. Here, in this paper we are proposing a model to automatically build Gujarati-English dataset from audio that is available in Gujarati Language through Speech Processing. © 2022 IEEE.

关键词： Deep learning

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： Software design

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Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

Dual-Band Planar Microstrip Monopole Antenna Design Using Mu...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Hosseini, V. Shapour, F. Pinho, P. Farhang, Y. Majidzadeh, K. Ghobadi, Ch. Nourinia, J. Barshandeh, S. Shokri, M. Amiri, Zh. Jalilirad, M. Kaboutari, K. Payame Noor University Department of Computer Engineering and Information Technology Tehran Iran Islamic Azad University Urmia Branch Computer Engineering Department Urmia Iran Instituto de Telecomunicações Aveiro Portugal University of Aveiro Department of Electronics Telecommunications and Informatics Aveiro Portugal Islamic Azad University Khoy Branch Department of Computer Engineering Khoy Iran Urmia University Department of Electrical Engineering Urmia Iran School of Engineering Department of Computer Science Afagh Higher Education Institute Urmia Iran

ISBN: (纸本)9798350312843

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized S11 be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using computer Simulation technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables. © 2023 IEEE.

关键词： Monopole antennas

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Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Hybrid-RIS Empowered UAV-Aided ISAC Systems 100

Hybrid-RIS Empowered UAV-Aided ISAC Systems

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Saikia, Prajwalita Jee, Anand Singh, Keshav Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi110016 India University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece

ISBN: (纸本)9798331517786

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Price Prediction of Seasonal Items Using Time Series Analysis

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computer Systems Science & engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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Sarcasm detection in product reviews using textual entailment approach 23

Sarcasm detection in product reviews using textual entailmen...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Shubham Sinha Tarun Vijeta Pratik Kishor Kubde Ayush Pradeep Gajbhiye Mansi Anup Radke Christopher Jones Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India School of Computer Science and Informatics Cardiff University UK

ISBN: (纸本)9798400709227

Sarcasm is a form of sentiment characterized by the use of words that express the opposite of what is meant. Sarcasm detection has applications in multiple domains ranging from sentiment analysis in product reviews to user feedback, and online forums. Sarcasm detection is important to understand user opinions and intentions in areas such as sentiment-based classification and opinion mining. This can result in better product development and customer service. Sarcasm detection can be a challenging task because sarcastic sentences may use positive expressions to convey negative meanings or may use negative sentences to convey positive meanings. Also, sarcastic sentences form a very small component of the entire communication. The increasing use of sarcasm in various social media such as Twitter, Reddit, Amazon product reviews, etc. has highlighted the importance of detecting and understanding sarcasm in various contexts. Sarcasm detection is a challenging problem for NLP systems that often rely on statistical models for performing sentiment analysis. In this research, the focus is on the use of a textual entailment approach for detecting sarcasm. Textual entailment is a natural language inference task that involves determining whether one text (hypothesis) can be derived from another text (premise). The underlying assumption behind this approach is that - if there is a contradiction between the premise and hypothesis, we can say that the hypothesis is sarcastic. To test our approach, an annotated corpus of 3000 product reviews was developed methodically from the Amazon Reviews dataset and tested using the textual entailment approach. The proposed approach achieved an F1 score of 0.76 on this dataset. The result is better than the baseline considered which is the BERT binary classifier which gives an F1 score of 0.48 on the same dataset.

关键词： Hypothesis Natural Language Inference Premise Sarcasm Detection Textual Entailment

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Data Science System of Predicting and Detecting the Chikungunya Virus using Ensemble Techniques 4

Data Science System of Predicting and Detecting the Chikungu...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Reddy, Bommireddy Vijay Kumar Patel, Shail Singh, Anayna Singh, Nishant Ranjit, Sasanapuri Dantkale, Shreyas National Institute of Technology Srinagar Department Computer Science and Engineering Jammu and Kashmir India Vellore Institute of Technology Department Computer Science and Engineering Vellore India Niit University Department Computer Science and Engineering Neemrana India University of Delhi Department of Institute of Informatics and Communication Delhi India Pes University Department of Electronics and Communication Engineering Bangalore India Vishwakarma Institute of Technology Department of Electronics and Communication Engineering Pune India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

The ailment known as chikungunya fever is brought on by a virus that mosquitoes disseminate. Due to the infection's excruciating joint and muscular discomfort, the term translates to 'bends you up' or 'stooped walk.' The abrupt onset of a high temperature, headache, exhaustion, rash, nausea, and red eyes are other symptoms. Chikungunya symptoms frequently show up 2 to 7 days after being bitten by an infected mosquito. There isn't a vaccination yet. Additionally, there is no cure for the illness. However, fatalities or major complications with chikungunya are uncommon. Many people recover in a matter of weeks. However, some people experience joint and muscular discomfort that can linger for months or even years after they have been exposed to the virus. After one to two weeks, a lot of people are free of further problems. Others, though, can experience joint and muscular discomfort for years. This kind of arthritis is known as chronic chikungunya. At least 40persons who contract the virus are affected. Chikungunya is seldom fatal. But for certain people, the infection can lead to serious issues. Older persons, those with chronic illnesses like diabetes or high blood pressure, small children, and pregnant women who could pass the virus to their unborn children are at increased risk. Serious issues with the eyes, heart, and nerves can be complications. Once-infected individuals are likely to be resistant to subsequent infections. The main aim of the authors is to use the ensemble technqiue to find a novel approach and to compare the ensemble techniques. © 2022 IEEE.

关键词： Viruses

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Gerasimos Vonitsanos Andreas Kanavos Foteini Grivokostopoulou Spyros Sioutas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Computer Technology Institute and Press “Diophantus“ Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting.

关键词： Deep learning Biological system modeling Time series analysis Predictive models Multilayer perceptrons Economic forecasting Big Data applications Cryptocurrency Context modeling

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