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1st edition of the International Conference on Artificial Intelligence and Green Computing, ICAIGC 2023

作者： Hamout, Hamza Elyousfi, Abderrahmane Computer Systems and Vision Laboratory Faculty of Sciences Ibn-Zohr University Agadir80000 Morocco Department of Computer Science National Engineering School of Applied Sciences Ibn-Zohr University Agadir80000 Morocco

ISBN: (纸本)9783031465833

In 2016, ITU-T Video Coding Experts Group (VCEG) and ISO/IEC MPEG finalize the recent 3-dimensional video coding standard founded on Multi-view texture Videos plus Depth maps (MVD) format called, the 3D extension of High-Efficiency Video Coding (3D-HEVC). In the current design of 3D-HEVC, the conventional intra-prediction modes and Depth Modeling Modes are exploited sophistically to improve the depth map coding performance. However, the coding efficiency improvement is achieved at the cost of computational complexity load, which obstructs 3D-High Efficiency Video Coding standards from practical world applications. In the 3D-HEVC, the depth maps are described by enormous homogeneous regions, and it is observed that for most of the cases, the DMMs and all or the majority of angular intra modes could be skipped for the homogenous area. Thus, we propose an efficient intra-coding model decision for a depth map founded on the clustering algorithm and edge detection to resolve the above-mentioned complexity increase. Experimental results demonstrate that the proposed intra-coding model decision for depth map can decrease the encoding time up to 41.35%, with insignificant loss of rate-distortion performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge detection

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Automated Text-based Depression Detection using Hybrid ConvLSTM and Bi-LSTM Model 3

Automated Text-based Depression Detection using Hybrid ConvL...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Firoz, Neda Beresteneva, Olga Grigorievna Vladimirovich, Aksyonov Sergey Tahsin, Mohammad Sadman Tafannum, Faiza Tomsk State University Institute of Applied Mathematics and Computer Science Tomsk Russia Tomsk Polytechnic University Department of I.T Institute of Applied Mathematics and Computer Sciences Tomsk Russia Brac University Department of Computer Sciences and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665462167

Depression and its symptoms are very common disorders of mental health. They affect the day-to-day activity of the person and degrade the quality of life. The article presents the comparative study of different deep learning models on natural language processing data for detection of depression using textual data. Several studies have been performed for depression detection using artificial intelligence and deep learning state of the art methods. This article investigates the state-of-the-art models and perform hyperparameter tuning for best accuracy results and develop our own hybrid model for detection of depression with improved accuracy scores. The aim of our study is to research and compare the existing findings in deep learning and machine learning models for depression detection and build a precise hybrid model for depression detection with higher accuracy and scores. © 2023 IEEE.

关键词： Long short-term memory

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Evaluation and Comparison of a Private and a Public Blockchain Solution for Use in Supply Chains of SMEs Based on a QOC Analysis 4th

Evaluation and Comparison of a Private and a Public Blo...

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4th International Congress on Blockchain and Applications, BLOCKCHAIN 2022

作者： Carls, Vanessa Schmidt, Lambert Jansen, Marc Institute of Computer Science University of Applied Sciences Ruhr West Bottrop46236 Germany

ISBN: (纸本)9783031212284

SMEs (Small and Medium Enterprises) are more and more interested in using blockchain technologies nowadays and one of the first questions that arises when dealing with the subject of blockchains is whether to choose a private or a public blockchain. This research utilizes requirements of a blockchain solution of three different SMEs for a QOC analysis (Questions, Options and Criteria) and consults the opinions of three blockchain experts to make a decision between a private and a public blockchain based on these evaluations and the corresponding weightings from the interviewed SMEs. In addition, the mentioned criteria also highlight IT requirements that are important for SMEs with regard to blockchain applications. Considering the specified requirements, their weightings, and the evaluation of the two given alternatives, it turns out that a public blockchain is preferable for this particular use case. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain IT requirements Private Public QOC analysis SME Supply chain

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Model for the calculation of soil compaction on agricultural land 44

Model for the calculation of soil compaction on agricultural...

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Die 44. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft 2024 - 44th Annual Conference of the German Association for Informatics in Agriculture, Forestry, and the Food Sector 2024

作者： Westerkamp, Clemens Thünemann, Christian Schaarschmidt, Marco University of Applied Sciences Osnabrück Institute of Computer Engineering Albrechtstr. 30 Osnabrück49076 Germany

ISBN: (纸本)9783885797388

The risk of soil compaction is a growing concern in agriculture as machinery becomes larger and larger. In this paper, a model is presented that generates a spatial estimation of the soil compaction based on soil survey mapping, soil moisture data and machinery data. The Soil Compaction Index describes the risk of harmful compaction of soil. Feasibility and deployment as an Agri-Gaia service were evaluated by an application for researchers and practitioners to predict areas with high soil compaction risk and adapt agricultural processes accordingly. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Soil surveys

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E-Sports Player Performance Metrics for Predicting the Outcome of League of Legends Matches Considering Player Roles

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SN computer Science 2023年第3期4卷 238页

作者： Bahrololloomi, Farnod Klonowski, Fabio Sauer, Sebastian Horst, Robin Dörner, Ralf RheinMain University of Applied Sciences Wiesbaden Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

Predicting results in electronic sports (e-sports) matches is not an easy task. Different methods can be used for this purpose. A well-known video game in the field of Multiplayer Online Battle Arena (MOBA) is the game League of Legends (LoL), which has a relevant professional scene. An important part of professional gaming is analyzing past matches overall and an individual player’s performance to prepare for future matches. In this paper, we follow a design-oriented research methodology (analysis, design, and evaluation) and propose performance metrics that use data from past matches to evaluate a player’s performance. We analyze the necessary data which we acquire by selecting a player, analyzing the player’s latest games, and repeating the process recursively with the players found in his latest games. The data is utilized within a Machine Learning (ML) Model that computes an overall score from individual player variables. From this, we designed a heuristic approach and evaluated it by applying it to the challenge of winning predictions in e-sports. The difference in the influence of the individual player roles on the outcome of the game was also investigated. It was found that this difference is negligible and that the heuristic performance metric can predict the outcome of a game with an accuracy of 86%. Furthermore, the concept of a match calculator is explored, which calculates the outcome of a match using the ML model and different player stats. © 2023, The Author(s).

关键词： computer games Data science Electronic sports (e-sports) League of Legends Machine Learning Performance analysis Player modeling Winning prediction

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A quantitative study of gender representation and authors’ gender in a large-market print medium

A quantitative study of gender representation and authors’ ...

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2024 Computational Humanities Research Conference, CHR 2024

作者： Bartl, Christoph Rezagholi, Sharwin Schumacher, Mareike Department Computer Science University of Applied Sciences Technikum Wien Austria Institute of Literary Studies University of Stuttgart Germany

We analyse gender representation in articles published by the Austrian daily newspaper’Der Standard’ in the years 2021 and 2022. We use named entity recognition and automated gender classification of first names to count the number of female and male persons in articles. The analysis reveals the dominance of male persons in article content. We find that female authors exhibit a significantly higher tendency to mention female persons in their articles. © 2024 Copyright for this paper by its authors.

关键词： Gender of journalists gender representation in articles print newspaper content

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A Machine Learning-Based Approach for Network Optimization in WSNs

A Machine Learning-Based Approach for Network Optimization i...

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International Conference on Data Science, Computation and Security, IDSCS 2023

作者： Bhattad, Bharti Sethi, Kamal Kumar Pal Singh Rathore, Narendra Bhanodia, Praveen Mishra, Durgesh Kumar Computer Science Engineering Acropolis Institute of Technology and Research Indore India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

With the advancements in interconnected devices and automation, wireless sensor networks have gained substantial importance. Energy constraint is one of the main issues that wireless sensor networks (WSNs) must deal with. Moreover, to enhance the effectiveness, it is necessary to reduce the latency as much as possible. Wireless sensor networks can be optimized for latency and energy consumption using a variety of protocols, with their own pros and cons. This paper investigates different aspects related to wireless sensor networks along with the relevant approaches. The intent of this paper is to present a reinforcement learning-based approach for optimized routing in WSNs. The protocol is designed to cater to the constraints of limited energy and latency, with the evaluation of the proposed algorithm in terms of network lifetime and latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Spread Analysis and Prediction of Covid-19 in India using Machine Learning

Spread Analysis and Prediction of Covid-19 in India using Ma...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Yenkikar, Anuradha Babu, C. Narendra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350396485

Since its breakout, the coronavirus (covid-19) has wreaked devastation all over the world. There hasn't been a nation that hasn't been affected by it, and India is no exception. Finding a treatment for this sickness and stopping its spread is one of the hardest problems humanities has ever faced. World Health organization (WHO) figures show that the mortality rate and the number of people with covid-19 are both increased exponentially in some countries during the peak waves. In this study, we use machine learning approaches to forecast the number of covid-19 confirmations, recoveries, and mortality cases over a period of time and assess the coronavirus outbreak in India. The polynomial regression (PR), support vector regression (SVR), and an autoregressive integrated moving average (ARIMA) model are three techniques that are used. The findings demonstrate that, in terms of prediction outcomes, the ARIMA model provides the least Root Mean Squared Error (RMSE), closely followed by polynomial regression. SVR doesn't perform well since predictions are either too low or too high. Overall, the proposed system can significantly aid in comprehending the pattern of spread in other nations and assist governmental bodies in taking action to lessen its effects in future. © 2023 IEEE.

关键词： Mean square error

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