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Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip Selection Approach

Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip S...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alshaikhli, Mays Al-Maadeed, Somaya Saleh, Moutaz Qatar university Dept. Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350367560

The IOTA Tangle represents a promising alternative to blockchain technology, particularly for the Internet of Things (IoT) environment. However, ensuring efficiency and fairness in transaction processing remains a significant challenge. This paper focuses on the efficiency of the Tangle by evaluating a novel POMDP-based Tip Selection Algorithm (TSA). Through comparative analysis against Weighted TSA, G-IOTA, and E-IOTA under varying network loads (lambda values), the POMDP algorithm demonstrates superior performance in maintaining the fastest confirmation process and more efficiency in different network conditions. These results establish POMDP as a scalable and efficient solution, contributing to the advancement of decentralized ledger technology. Future directions include real-world testing and the integration of adaptive network factors for enhanced scalability. © 2024 IEEE.

关键词： Blockchain

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Integrating Tradition and Technology: A Survey of AI Applications in the Design of Temple Architecture

Integrating Tradition and Technology: A Survey of AI Applica...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Meghana, J. Bellad, Harshavardana R. Pardhi, Diya Magdum, Ishwari Nagaraj, Chaitra Dept. Computer Science Engineering PES University Bengaluru India

ISBN: (纸本)9798350353488

This article will delve into the complicated inter-building of artificial intelligence and traditional skill of temple architecture of South India. It meticulously analyzes the historical roots of temple design, explaining in detail the subtle means and methods that have always been used in temple construction. Apart from detailed preparation and skilled craftsmanship, it is the cultural heritage that is demonstrated with these monumental creations. In the sequel, the paper sheds light on how AI technologies are transforming temple architecture through systems analysis and optimization, generative design and other methods. Living examples of Artificial Intelligence work do not only confirm the successful AI implementations but also illustrate the smooth link between tradition and novelty in the modern temple designs. © 2024 IEEE.

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Data Analysis of Dark Web Marketplaces using Machine Learning 7

Data Analysis of Dark Web Marketplaces using Machine Learnin...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Pratham Rao, U.N. Guruprasad, Rachit Raam Shetty, Omkaar J Sarasvathi, V. Rapate, Gauri Sameer PES UNIVERSITY Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368673

People refer to the dark web as the anonymous part of the internet that is very hard to access, it has been described as lying some distance beyond the easily accessible surface web and more obscure deep web. This means that the dark web is accessed by using specially designed software, and it hosts all manner of illicit activities that present huge challenges to law enforcement. This section, known as the dark web, may only be accessed with the use of special designed programs, such as Tor. In this sphere of the net, it is feasible for both the user and the owners of any site to claim anonymity. The dark web is still notorious for offering illegal activities such as drugs markets, selling firearms, and providing services in cybercrime, though it is on the gray area of applying it towards journalists, social activists, and whistleblowers. But it is pretty much important to note here that the deep web or parts of the internet and the dark web are very much different things. In order to shed some light on how to overcome these challenges, we have come up with a custom web crawler specifically designed to work inside the Tor network, extracting data from various dark web marketplaces. In this paper, we will be focusing on the profiling of vendors according to categories and geographical origins, with an emphasis on operational patterns of these cloaked entities. To sum it all, our customized web crawler provides great insights about profiling vendors in dark web markets and categories where they originated. © 2024 IEEE.

关键词： Marketplaces

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Enhancing Network Security with AdvancedNetwork Scanning Tools 6

Enhancing Network Security with AdvancedNetwork Scanning Too...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Patel, Shubh Christian, Prarthan Mistry, Kartikay Raj, Krenil Raithatha, Hiren Dept. Computer Science and Engineering Parul University Vadodara India

ISBN: (纸本)9798350369748

This research paper introduces a sophisticated network scanning tool designed to enhance cybersecurity measures by integrating advanced techniques in operating system (OS)detection, service scanning, and intrusion detection system (IDS) / intrusion prevention system (IPS) detection. The tool aims to provide a comprehensive approach to network security assessment, offering robust capabilities for identifying potential vulnerabilities and mitigating security risks. The OS detection module employs a combination of fingerprinting techniques and heuristics to accurately identify the underlying operatingsystems of networked devices. This information is crucial forunderstanding the network environment, enabling administrators to implement targeted security measures. The tool incorporatesa sophisticated IDS/IPS detection mechanism to identify and evaluate the effectiveness of intrusion detection and prevention systems in place. This feature is vital for assessing the net- work’s resilience against potential threats and ensuring that the deployed security mechanisms are robust and up-to-date. The research paper details the methodology behind each scanning module, highlighting the innovation and integration of cutting- edge technologies. Additionally, practical use cases and real-world scenarios are presented to demonstrate the tool’s effectiveness in identifying and addressing security concerns in diverse network environments. © 2024 IEEE.

关键词： Network intrusion

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DevOps Practices Into Machine Learning 3

DevOps Practices Into Machine Learning

引用

3rd IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Jain, Samridhi Kumar, Puneet Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350304329

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners. © 2024 IEEE.

关键词： DevOps Machine Learning MLOps MLOps tools

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Spatial Data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Sujit Kumar Tyagi, Neha Amity University Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798350383522

The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much more significance than oil itself, unlike the British mathematician Clive Humby's quote in 2006 'data is the new oil'. Every business and processes generate data and with the advent of AI and machine learning, all types of data have valuable use case attached. This also ensued a series of initiatives to clean, augment and provide data as data-products. This has given rise to businesses and solutions using inhouse data or data purchased from a dedicated data provider. Data consumers often face the challenge of inefficient data product discovery. More frequently than not they require active support from data product experts to point them to right dataset. The semantic meaning of the attributes and keywords of a data product is also heavily domain oriented. Data providers find it easy to update and augment metadata of a data set with details more feasible solution than modifying the dataset itself with attribute rename or adding composite attribute. The expected next step is that the metadata be self-explanatory enough to help in data cataloguing, but often we find consuming data scientist looking for help from data experts to make sense of the data in the context of the consumer's use case. This research seeks to analyse if the role of a data expert can be reduced and a finetuned small language model can help a data consuming data scientist in discovering the right data product, based on the metadata of the data product. Overall, this research contributes to the advancement of data discovery techniques and highlights the potential of leveraging language models to streamline and democratize access to valuable data resources, thereby empowering organizations to make more informed and data-driven decisions. © 2024 IEEE.

关键词： data discovery finetuning llama2 small llm spatial data

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LG-FAL: Locality-customized GSA Federated Active Learning 15

LG-FAL: Locality-customized GSA Federated Active Learning

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Wang, Shuwen Zhu, Xingquan Portland State University Department of Computer Science Portland United States Florida Atlantic University Dept. of Electrical Engineering & Computer Science Boca Raton United States

ISBN: (纸本)9798331508821

Federated Learning (FL) is a learning paradigm which constructs machine learning models using decentralized datasets, with the goal of training a globally optimized model while preserving data privacy. In practice, annotating training data for FL is expensive and time-consuming. To solve this problem, researchers proposed to integrate active Learning (AL) strategy as Federated Active Learning (FAL) framework. However, existing research on FAL has two main limitations: (1) In the active learning part, more attention is paid to the global model, while ignoring the local information;(2) In the federated learning part, the popular Federated averaging (FedAvg) method relies on the assumption that the corresponding nodes in distributed neural networks share the same importance when averaging. In this paper, in order to tackle the two limitations, we propose a locality-customized GSA federated active learning (LG-FAL) method. Specifically, (i) both local and global information are taken into consideration to evaluate and select informative data samples to annotate for active learning;(ii) we integrate Gravitational Search Algorithm (GSA) to dynamically average local network parameters into the global parameter, by mimicking the principles of gravity and motion in the universe, for effective federated learning. As a result, LG-FAL can select a small subset of informative samples considering both local and global information, and at the same time, provide an improved trade-off between communication cost and learning accuracy. Experimental results show that LG-FAL significantly outperforms the current state-of-the-art baselines in terms of performance and effectiveness. © 2024 IEEE.

关键词： Federated learning

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Analytical Survey of Domain Effective Sentiment Lexicon for Aspect-Centric Opinion Analysis 8

Analytical Survey of Domain Effective Sentiment Lexicon for ...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Purbey, Lal Babu Lakhwani, Kamlesh Jecrc University Dept. Computer Science of Engineering Jaipur India

ISBN: (纸本)9798350391770

Aspect-level sentiment analysis is a modern technology that enables people to share their views or emotions with products, services, or topics based on their aspects. Aspect-level sentiment analysis focuses on aspect extraction, which considers aspects from user reviews. Various methods and approaches have been employed for aspect extraction. Although these methods show better performance and attempt to overcome various challenges, many unsolved problems still need to be solved, such as sentimental ambiguity, opinion misprediction, a weak sentiment lexicon, and implicit sentiment detection. Numerous sentiment lexicons have been employed for word polarity extraction. Dictionaries also play an important role in sentiment classification. There are various dictionaries, but it has been found that issues such as one dictionary might not work properly for all domains. This study analyzes various existing sentiment lexicons employed for domain-specific sentiment analysis. Various approaches have been employed to create a domain-dependent sentiment lexicon and its effect on aspect-level sentiment analyses. © 2024 IEEE.

关键词： Aspect Extraction Aspect level sentiment analysis Domain dependent dictionaries Sentiment Analysis Sentiment Lexicon

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A Hybrid Model for Deciphering Doctors' Handwriting Notes Recognition 6

A Hybrid Model for Deciphering Doctors' Handwriting Notes Re...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Khanal, Santosh Bista, Rabindra Kathmandu University Dept. of Computer Science and Engineering Kavre Nepal

ISBN: (纸本)9798350389692

Handwritten Text image Recognition (HTR) in the medical profession has become the major challenge due to the complexity of doctors' handwriting styles and the critical need for precise and efficient text recognition. This study proposes an innovative approach that combines the power of Bidirectional Long Short-Term Memory networks (BLSTM) and Convolutional Neural Networks (CNN) to address these challenges effectively. The proposed model harnesses the bidirectional capabilities of BLSTMs to capture contextual dependencies within doctors' handwritten notes, enabling it to understand and interpret handwriting more accurately. Additionally, CNNs are employed for feature extraction, enabling the model to recognize salient patterns and representations within handwritten text images. This paper presents comprehensive experiments conducted on a diverse dataset of doctors' handwritten notes, demonstrating the model's superior performance compared to conventional approaches and outperforms the existing work in terms of accuracy. Two different crucial metric word error rate and character error rate are accessed through the combination of BLSTM and CNN that yields state-of-the-art accuracy, robustness to variations in handwriting styles, and remarkable adaptability across various medical documents in the context of Nepal. © 2024 IEEE.

关键词： Convolutional neural networks

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