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Comparative analysis of distance metrics for intuitionistic fuzzy sets in multi-criteria decision analysis

Procedia computer science 2024年 246卷 5418-5427页

作者： Jakub Więckowski Daniel Niewiadomski Dominik Kulis Wojciech Sałabun National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. żołnierska 49 71-210 Szczecin Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 31-155 Krakow Poland

This paper presents a comparative analysis of selected distance metrics within the intuitionistic fuzzy environment in Multi-Criteria Decision Analysis (MCDA). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, combined with intuitionistic fuzzy sets (IFS), was employed to investigate how different distance metrics influence decision-making outcomes. The study examines the sensitivity of the obtained recommendations to the variation in the calculation formula used to determine the distance between two IFS. The practical problem of selecting a site for a large-scale rooftop photovoltaic project served as the case study for this comparison. The results indicated that using different distance metrics within the IF-TOPSIS method led to varying preference score ranges and differing rankings, particularly in the middle portions of the rankings. The findings underscore the sensitivity of MCDA methods to the choice of distance metrics used in the calculation process.

关键词： distance metrics intuitionistic fuzzy sets decision-making results robustness sensitivity analysis

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Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study

Efficacious Routing Approaches in Vehicular Ad hoc Network: ...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： J Jesy Janet Kumari S. Thangam A. Saleem Raja Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham India Department of Computer Science and Engineering University of Technology and Applied Science-Shinas Oman

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques.

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Innovations in Agri-Tech: A Review of Artificial Intelligence Applications and Challenges in Modern Agriculture

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Rupanshi Agarwal Isha Bhardwaj Ashish Kumar Sharma Akash Sanghi Gaurav Agarwal Department of Computer Science and Engineering Invertis University Bareilly India Department of Applied Science and Humanities Kothiwal Institute of Technology and Professional Studeis Moradabad India Department of Computer Applications Invertis University Bareilly India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties.

关键词： Precision agriculture computer vision Technological innovation Reviews Food security Machine learning Stability analysis Internet of Things Predictive analytics Sustainable development

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Application of Machine Learning in Mental Diseases Diagnosis

Application of Machine Learning in Mental Diseases Diagnosis

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Information technology (ACIT)

作者： Rahaf Alsawi Raed Alazaidah Ghassan Samara Haneen Alzoui Omar Alidmat Esraa Abu Elsoud Mohammed Talat Khouj Anwar Katrawi Mo’Ath Alluwaici Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient’s way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person’s mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method.

关键词： Measurement Logistic regression Mood Machine learning Schizophrenia Feature extraction Depression Bayes methods Medical diagnosis Information technology

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Application of COPRAS, PROMETHEE, and EDAS methods in sustainable energy development: A comparative study case

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Procedia computer science 2024年 246卷 5428-5438页

作者： Jakub Więckowski Paweł Gajewski Krzysztof Swałdek Wojciech Sałabun National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 31-155 Krakow Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. żołnierska 49 71-210 Szczecin Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods.

关键词： multi-criteria decision analysis comparative analysis sustainable development renewable energy sources

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Framework for Enhanced Digital Image Transmission Security: Integrating Hu Moments, Digital Watermarking, and Cryptographic Hashing for Integrity Verification 2

Framework for Enhanced Digital Image Transmission Security: ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Dorgham, Osama Aburass, Sanad Issa, Ghassan F. School Of Information Technology Skyline University College Sharjah United Arab Emirates Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty Of Ict Al-Salt Jordan Maharishi International University Department Of Computer Science FairfieldIA United States

ISBN: (纸本)9798350394962

Image stability is very important in a time when digital image communication is essential to many fields. Modern online dangers are often too complicated for old security methods to keep up with. To solve these problems, this study presents a new system that combines Hu moments, digital watermarking, and cryptography hashing. Hu moments create a unique graphic stamp that can be used to check the image after it has been received. Digital watermarking increases the integrity of information because it involves a code that cannot be seen but can be detected to make changes impossible. The digital fingerprint is created with such hashing algorithms as SHA-2s and other cryptographic hash functions before the message transmission. This fingerprint can be utilized to verify the integrity of a message when it arrives at its destination. Our combined method provides a comprehensive defense against image hacking, guaranteeing the accuracy of images sent over networks that might not be fully safe, the structure is made to be invisible, protecting image quality while offering strong security against changes. We will explain how this whole system was put together, how it will be used, and how it should be evaluated. We also discuss how it could be used in many situations where the quality of digital images is very important. © 2024 IEEE.

关键词： Image enhancement

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Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

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computer Systems science & Engineering 2023年第10期47卷 1283-1297页

作者： Mohammed Altaf Ahmed T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Seifedine Kadry Yena Kim Yunyoung Nam Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of ICT Convergence Soonchunhyang UniversityAsan 31538Korea

Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to *** the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring *** clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision *** clustering protocol has several *** a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more *** article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in *** JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack *** presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network *** JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain *** addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the *** proposed JSOUCP-MHP technique is examined under several experiments to ensure its *** comparison study shows the significance of the JSOUCPMHP technique over other models.

关键词： Wireless sensor networks energy efficiency cluster heads unequal clustering hot spot issue lifetime enhancement

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Social mining for sustainable cities: thematic study of gender-based violence coverage in news articles and domestic violence in relation to COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14631-14642页

作者： Manzoor, Muhammad Asad Hassan, Saeed-Ul Muazzam, Amina Tuarob, Suppawong Nawaz, Raheel Department of Computer Science Information Technology University 346-B Ferozepur Road Lahore Pakistan Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Applied Psychology Lahore College for Women University Lahore54000 Pakistan Faculty of Information and Communication Technology Mahidol University Salaya Thailand Department of Operations Technology Events and Hospitality Management Manchester Metropolitan University Manchester United Kingdom

We argue that social computing and its diverse applications can contribute to the attainment of sustainable development goals (SDGs)—specifically to the SDGs concerning gender equality and empowerment of all women and girls, and to make cities and human settlements inclusive. To achieve the above goals for the sustainable growth of societies, it is crucial to study gender-based violence (GBV) in a smart city context, which is a common component of violence across socio-economic groups globally. This paper analyzes the nature of news articles reported in English newspapers of Pakistan, India, and the UK—accumulating 12,693 gender-based violence-related news articles. For the qualitative textual analysis, we employ Latent Dirichlet allocation for topic modeling and propose a Doc2Vec based word-embeddings model to classify gender-based violence-related content, called GBV2Vec. Further, by leveraging GBV2Vec, we also build an online tool that analyzes the sensitivity of Gender-based violence-related content from the textual data. We run a case study on GBV concerning COVID-19 by feeding the data collected through Google News API. Finally, we show different news reporting trends and the nature of the gender-based violence committed during the testing times of COVID-19. The approach and the toolkit that this paper proposes will be of great value to decision-makers and human rights activists, given the prompt and coordinated performance against gender-based violence in smart city context—and can contribute to the achievement of SDGs for sustainable growth of human societies. © 2022, Crown.

关键词： COVID-19

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Integrated Interaction Journey and Privacy Risk Assessment: A Graph Model

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Procedia computer science 2024年 239卷 1594-1603页

作者： Maha Alabsi Asif Gill Madhushi Bandara Faculty of Engineering and IT School of Computer Science University of Technology Sydney Sydney Australia Applied College Taibah University Al-Madinah Al-Munawarrah - Madina Saudi Arabia

Smart airports involve several applications and stakeholders to facilitate passenger journey. Passengers interact with those stakeholders and share their personal information using the smart airport applications. While the use of smart airport applications offers several benefits, it also puts passengers’ personal information at risk. This draws our attention to the need for identifying and understanding privacy risks with a view to protect passenger information at smart airports. Our earlier systematic literature review study revealed a gap in modelling passenger information privacy risks in the context of smart airports. This paper aims to address this gap by developing an ontology for interaction journey and privacy risk assessment modeling. The contribution of the proposed ontology is to bring new knowledge and understanding of privacy risks in the contemporary smart airport context. The development and evaluation of the proposed framework follows the Design science Research (DSR) method along with the ontology development techniques. The proposed ontology aims to assist privacy experts in modelling and analyzing privacy risks relevant to passenger information in the smart airport context.

关键词： smart airport Passenger journey information privacy risk assessment ontology

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A generative adversarial network-based data augmentation approach with transient vibration data

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Procedia computer science 2023年 225卷 1340-1349页

作者： Koenig Timo Cadau Luca Wagner Fabian Kley Markus Institute for Drive Technology University of Applied Sciences Aalen 73430 Aalen Germany Faculty for Electronics and Computer Science University of Applied Sciences Aalen 73430 Aalen Germany

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with a t-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings.

关键词： "Bearing Condition Monitoring Data Generation Spectrogram Generative Adversarial Network (GAN)"

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