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Global Aeronautical engineering and Satellite Technology (GAST), International Conference on

作者： Toufik Datsi Khalid Aznag Brahim Ait BenAli Karim Karbout Ahmed El Oirrak El Kharrachi Khayya Faculty of Sciences Semlalia Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh Morocco LTI Laboratory ENSA School-University Chouaib Doukkali EL Jadida Morocco Faculty of Sciences Semlalia Computer Science Engineering Laboratory Cadi Ayyad University Marrakesh Morocco

ISBN: (数字)9798350371598

ISBN: (纸本)9798350371604

Advancements in multimodal learning have experienced rapid growth over the past decade, particularly within various domains, with a significant emphasis on developments in computer vision. Multimodal data fusion has become increasingly prominent in the realm of image classification, where the integration of diverse data sources enhances the overall understanding and performance of classification models. This survey delves into the recent strides made in multimodal learning over the past decade, particularly within the field of image classification. Additionally, the paper undertakes a comparative study, critically evaluating the effectiveness and performance of different multimodal fusion approaches. The aim is to provide a comprehensive overview of the current state-of-the-art in multimodal data fusion for image classification and to identify key trends, challenges, and opportunities in this evolving field.

关键词： Surveys Adaptation models Satellites Navigation Soft sensors Data integration Data models

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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Patch-based CNN Models for Bone Marrow Edema Detection Using MRI

Patch-based CNN Models for Bone Marrow Edema Detection Using...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： André Gomes Tania Pereira Francisco Silva Pedro Franco Diogo Costa Carvalho Sílvia Costa Dias Hélder P. Oliveira INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal CHUSJ - Centro Hospitalar e Universitário de São João Porto Portugal Faculty of Medicine University of Porto Porto Portugal

Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME.

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Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

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Parameter fault estimation in distributed heating/cooling systems 22

Parameter fault estimation in distributed heating/cooling sy...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Wijaya Kurniawan Katalin M. Hangos Lorinc Marton Faculty of Computer Science University of Brawijaya Indonesia) and Departement of Electrical Engineering and Information Systems University of Pannonia Hungary Departement of Electrical Engineering and Information Systems University of Pannonia Hungary and Institute for Computer Science and Control Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Romania)

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon.

关键词： Nonlinear Observer Heating/cooling system Fault diagnosis Structural observability Parameter estimation

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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Web-based framework for smart parking system

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International Journal of Information Technology (Singapore) 2021年第4期13卷 1495-1502页

作者： Alharbi, Awad Halikias, George Yamin, Mohammad Abi Sen, Adnan Ahmed Department of Electrical and Electronic Engineering School of Mathematics Computer Science and Engineering London United Kingdom Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia Faculty of Computer and Information Systems Islamic University Madinah Saudi Arabia

Smart cities still promise a lot for residents, despite the millions of smart services and applications that have changed many of our lifestyles for the better in various fields such as transportation, health, education, services, business, and others. However, users want more of these services that make their lives easier and more sophisticated. Traffic congestion is one of the most common problems that smart cities seek to solve by employing many IoT technologies and artificial intelligence algorithms, in addition to costly infrastructure development processes. This research highlights that parking in some streets and vital places in cities is an important factor in traffic congestion. It also introduces the idea of an intelligent system to solve this problem by enabling the automatic pre-reservation process for vacant positions by users before reaching the venue and then adjusting the process based on automatic detection of the composite plate using a proposed web application based on the OCR algorithm. The application has been implemented to confirm the ease of applying this system in some streets of the Kingdom of Saudi Arabia to reduce traffic congestion and improve the level of transport sector services as well as the level of safety of citizens. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Crowd IOT Parking RFID Smart city Traffic Vehicle WSNs

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Metagenomic Data Analysis with Machine Learning to Discover Colorectal Cancer-Associated Enzymes

Metagenomic Data Analysis with Machine Learning to Discover ...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Nur Sebnem Ersoz Cihan Kuzudisli Malik Yousef Burcu Bakir-Gungor Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The human gut microbiome comprises over 10 trillion microbes and plays important roles in maintaining metabolism, body homeostasis, impacting immune function. Metagenomics which studies genomic data from clinical and environmental samples is crucial in understanding the interplay between the host and the gut microbiome. Recently, functional profiling of metagenomes helps to identify alterations in microbial functions, particularly enzyme-encoding genes. Colorectal cancer (CRC) is known as one of the leading causes of cancer-related deaths. In this study, we aimed to find the CRC-associated enzymes by analyzing metagenomic data with different machine learning methods. A total of 1262 samples including CRC and control groups from different countries were used in this study. This dataset was obtained by functionally profiling metagenomics data and estimating community level enzyme commission (EC) abundance values. For the analysis of this dataset, RCE-IFE and SVM-RCE machine learning methods, which are group-based feature selection methods, were compared with 6 different individual feature selection methods. 10 times Monte-Carlo Cross Validation was used in our experiments. It was observed that RCE-IFE, Extreme Gradient Boosting and Select K Best methods similarly provided the best performances. Especially in this study, besides the its high performance, the group-based feature selection method RCE-IFE grouped enzymes into clusters unlike TFS, and then identified biologically relevant CRC-associated enzymes.

关键词： Enzymes Metagenomics Microorganisms Monte Carlo methods Colorectal cancer Signal processing Homeostasis

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Securing Patient Information in Connected Healthcare systems in the Age of Pervasive Data Collection

Securing Patient Information in Connected Healthcare Systems...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Ace Dimitrievski Tatjana Loncar-Turukalo Vladimir Trajkovik Faculty of Computer Science and Engineering Skopje Macedonia Centre of Excellence for Vibro-Acoustic Systems and Signal Processing Faculty of Technical Sciences University of Novi Sad Serbia

This paper explores the ethical and security challenges of integrating Internet of Things technologies into health-care systems. We examine real-world ethical dilemmas, including privacy and security of personal and medical data. We also analyze security in connected healthcare systems, focusing on securing data transmission, preventing data exfiltration, and mitigating the risk of de-anonymization of data. We propose a fog-based architecture and data avoidance principle to address and minimize the risks to patient data, intending to navigate the ethical landscape of IoT in healthcare, ensuring patient privacy.

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