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共 39991 条记录，以下是181-190 订阅

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An Automatic Prediction of Elevator Capacity Using ML to Customize the Total Capacity 5

An Automatic Prediction of Elevator Capacity Using ML to Cus...

引用

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

作者： Talwandi, Navjot Singh Tripathi, Anubhav Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elevator systems by minimizing the waiting time experienced by passengers. By leveraging historical data and predictive models, this research aims to optimize elevator operations, considering factors such as traffic patterns, peak hours, and user demand. The implementation of machine learning algorithms enables the system to dynamically adjust elevator scheduling strategies, ultimately reducing waiting times and enhancing user experience. The findings highlight the potential of machine learning techniques in revolutionizing elevator management for improved passenger satisfaction and operational efficiency. © 2024 IEEE.

关键词： Arduino Elevator optimization kNN Machine learning Operational efficiency Passenger waiting time Predictive modeling Traffic patterns User demand

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FFDM - GNN:A Financial Fraud Detection Model using Graph Neural Network 1

FFDM - GNN:A Financial Fraud Detection Model using Graph Neu...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kesharwani, Abhishek Shukla, Prashant United University Dept of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350353648

Financial Fraud Detection Model (FFDM) is used to develop an advanced detection framework utilizing Graph Neural Networks (GNNs) to accurately identify fraudulent transactions within the transactions. Traditional fraud detection systems often struggle with the dynamic and intricate nature of fraudulent activities. In this paper, we introduce an innovative fraud de-tection model leveraging the power of Graph Neural Networks (GNN) to address these challenges. Our model synergistically combines the strengths of graph-based learning with deep neural networks to effectively capture the complex relationships and patterns inherent in financial transactions. By utilizing a multi-layered approach, our GNN model not only identifies anomalous patterns indicative of fraud but also adapts to evolving fraudulent tactics.A key feature of our model is the integration of node and edge features, which enhances the representation of transaction networks. © 2024 IEEE.

关键词： Decentralized finance

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NFT Marketplace: What are NFTs, and how does OpenSea succeed in acquiring the most of the NFT Space 1

NFT Marketplace: What are NFTs, and how does OpenSea succeed...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Thakur, Anil Divya, K. Verma, Sahil Kaur, Maninder Chandigarh University Dept. of Computer Science & Engineering Mohali India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Guru Gobind Singh College for Women Dept. of Computer Science & Applications Chandigarh India

ISBN: (纸本)9798350373042

Nowadays, the world is becoming smarter, so as the technology, the new era is on the boom, which is blockchain because of its different use cases and benefits. The blockchain is a growing list of records called blocks that are linked together with the use of cryptography. Because all servers on a network are corresponding and a significant concern in the types of network style, its data sources are managed through a peer-to-peer (p2p) network. The servers engage in negotiation and communication through the agreement method. The dispersed network is a network system in which information, software application, and computer system programs are spread out on greater than one node (computer systems). These nodes also depend on each other. It is only a p2p network that gets rid of a solitary factor of failing. It is a decentralized, dispersed, and unalterable journal to show electronic purchases. One of the concepts of blockchain is NFTs. These are digital assets with a unique identity and cannot be replicated. They can be used for a variety of purposes, such as in-game items, digital collectibles, or even to represent real-world assets like cars or houses. They can represent any digital asset or physical goods. NFTs can represent ownership and authenticity of any type of digital asset or physical goods. They can also represent a stake in the game, such as ownership in a casino or a share in a company. © 2024 IEEE.

关键词： Smart contract

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AI-Powered Early Detection of Genetic Disorders in Fetuses Using Machine Learning Models 1

AI-Powered Early Detection of Genetic Disorders in Fetuses U...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Eluri, Rama Krishna Manogna, Aila Chandana, Yamini Moturi, Sireesha Gayathri, Chennupalli Akhila, Tanniru Yuvasri, Kondepogu Dept of Computer Science and Engineering Andhra Pradesh Narasaraopet India

ISBN: (纸本)9798331518943

Pregnancy complications have significant effects on women and can threaten the health of an unborn child. Pregnant women have a great interest in issues related to fetuses. The aim of the project is to create a predicting instrument for anticipating the state of a fetus' health by studying its pulse, speed ups, mobility and as well as the contractions within the womb using computer-based learning methods applied to the database of Cardiotocography. In this study, Some examples of machine learning algorithms are Random Forest, Logistic Regression, Decision Trees, Support Vector Classifications and K-Nearest Neighbours were employed to process the dataset. Through using all implemented machine learning models, we managed to achieve an accuracy of 99% through Random Forest algorithm. Detecting early complications is vital, and this study shows how medical resource management can be improved by machine learning, thus saving time. © 2024 IEEE.

关键词： Random forests

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Principal Component Analysis and Deep Learning-Based Traffic Anomaly Detection in WSN

Principal Component Analysis and Deep Learning-Based Traffic...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Shakya, Vandana Choudhary, Jaytrilok Singh, Dhirendra Pratap Manit Dept. of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350348460

Wireless Sensor Networks (WSNs) have advanced quickly due to the fast expansion of wireless networks. Yet, because of their ease of use and versatility, security concerns have grown. This means that conducting research on intrusion protection in WSNs is now essential. Denial of Service (DoS) assaults are among the most common types of network attacks. They are dangerous because they take down the target network in order to accomplish their goal. Within WSNs, where devices function with limited resources, a denial-of-service attack has the potential to be disastrous. This research suggests a novel solution for WSNs, which are susceptible to assaults because to their devices' little storage capacity. To find abnormalities in DoS traffic within WSNs, the technique combines a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA). By detecting and reducing the effects of DoS assaults, and by utilising the complementary capabilities of PCA and DCNN in this particular situation, the goal is to improve the security of WSNs. Compared with other traditional DL architectures, the proposed model has a more simplified structure and better feature extraction capabilities. This special combination gives it the power to quickly identify anomalous network activity in WSNs devices, especially those with limited storage. Because of its lightweight design, the suggested model addresses the inherent resource limits and guarantees optimal performance in the context of WSNs. A variety of assessment measures, such as confusion matrices, different classification metrics, and Receiver Operating Characteristic (ROC) curves, are used to verify the effectiveness of the suggested model. These metrics are used to evaluate the model's categorization performance in a rigorous manner. Extensive experimental comparisons reveal that the small size of the proposed model outperforms other popular models for anomalous traffic detection with regards to classification performance

关键词： Deep Convolutional Neural Network (DCNN) Denial of Service (DoS) KNN Principal Component Analysis (PCA) WSNs

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Enhancing Artificial Intelligence and Machine Learning Understanding Through EnVision: A Virtual Reality Approach 1

Enhancing Artificial Intelligence and Machine Learning Under...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Patra, Louise Kumari, Manisha Gopalapillai, Radhakrishnan Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350365016

The quality of teaching and learning process can be improved through innovative methods like use of virtual reality. We introduce EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education. EnVision offers a unique approach where users build models through immersive learning experiences. Using interactive tutorials mixed with real-time data integration and visualization, users learn AI/ML concepts with ease. Through qualitative and quantitative methods, we evaluate EnVision's effectiveness and user satisfaction, contributing to the advancement of innovative educational gaming technologies. The evaluation results show that EnVision provides the AI/ML practitioners engaging learning experiences. © 2024 IEEE.

关键词： Virtual environments

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Cloud Based Data Analytics for Business Intelligence 2

Cloud Based Data Analytics for Business Intelligence

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Talwandi, Navjot Singh Shah, Akhilesh Kumar Thakur, Rakesh Khare, Shanu Thakur, Payal Dept of Computer Science Engineering Chandigarh University Punjab India

ISBN: (纸本)9798331541217

This comprehensive review paper delves deeply into the realm of cloud-based data analytics platforms, underscoring their crucial role in propelling Business Intelligence (BI) forward during an era characterized by an unprecedented surge in data volumes. Faced with the deluge of data, organizations are increasingly relying on these platforms as indispensable tools to tap into the hidden potential of data and facilitate well-informed decision-making. The paper methodically traces the evolutionary path of cloud- based BI analytics platforms, shedding light on their diverse advantages and the substantial challenges they pose. It also elucidates the emerging trends in this dynamic landscape, outlining how these platforms are poised to reshape the future of BI. By conducting a thorough review of the existing body of literature, this paper imparts valuable insights into the defining attributes of these platforms, effective strategies for their adoption, and the critical factors that underpin their effectiveness. Additionally, it explores the pivotal role these platforms playing enhancing the flexibility, scalability, and cost-efficiency of BI processes. Moreover, in addition to furnishing an in-depth analysis, this review identifies significant gaps in current research and provides thoughtful recommendations for future avenues of exploration. These recommendations are intended to promote the continued growth and refinement of cloud-based data analytics platforms within the broader context of BI. © 2024 IEEE.

关键词： Business Intelligence Cloud Storage Data Analytics Stock

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Qubits

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Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages 1

Advancing Multilingual Speaker Identification and Verificati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Sritharan, Braveenan Thayasivam, Uthayasanker Dept. of Computer Science & Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9798891762145

Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems. © 2025 Association for Computational Linguistics.

关键词： Speech recognition

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Triple-Node-Upset Recoverable Radiation-Hardened by Design Latch

Triple-Node-Upset Recoverable Radiation-Hardened by Design L...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Paparsenos, Evangelos Tsiatouhas, Yiorgos Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

CMOS technology evolution enhances integrated circuits (ICs) performance characteristics at the cost of their increased susceptibility to radiation and thus to the occurrence of single-event upsets (SEUs) that may lead to soft errors generation. SEUs may affect multiple nodes in a circuit (multiple node upsets - MNUs). Fortunately, these disturbances do not cause permanent damage to the circuits. Various techniques have been proposed to deal with SEUs that concurrently affect one, two or three nodes. In this paper, we propose the design of a latch that offers tolerance up to the level of triple-node upsets (TNUs). This is achieved by using redundancy in the hardware which provides the ability to store a logic value in multiple nodes within the latch as well as by using multiple feedback paths which allow it to recover its correct state in the event of an SEU. Compared to other techniques dealing with the same problem, our proposed method provides faster recovery time (higher than 16.87%) after an SEU, and at the same time reduced power consumption and higher speed performance. © 2024 IEEE.

关键词： Radiation hardening

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