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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66489 条记录，以下是4791-4800 订阅

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Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers

Machine Learning Techniques for Predicting Ovarian Cancer in...

引用

International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Mir Jafikul Alam Jannat-E Anawar Khandaker Mohammad Mohi Uddin Md. Hasibur Rahman Md. Mahbubur Rahman Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

The deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. The key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. The proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning.

关键词： Measurement Logistic regression Statistical analysis Forestry Predictive models Reliability Random forests

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Indian Stock Market Forecasting using Prophet Model 1

Indian Stock Market Forecasting using Prophet Model

引用

1st International Conference on Connected systems and Intelligence, CSI 2022

作者： Nair, Anjana V. Narayanan, Jayasree Amrita Vishwa Vidhyapeetham Dept. Of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781665458153

An inventory marketplace or a stock market is a platform for buying and selling a company's shares and derivatives at an agreed price. The goal of Stock Market Prediction is to forecast the fate fee of a company's economic shares. The latest development in market analysis technology is the use of machine earning to determine the values of current exchange indexes based on their prior values. The project entails determining the future prices of the stock markets by selecting the data from the available dataset and then determining the future pricing based on the user's desired duration of selection of months. It's built with the Prophet API and deployed with the Streamlit framework locally. Whose performance is then assessed using metrics such as Mean Squared Error and Root Mean Squared Error © 2022 IEEE.

关键词： Financial markets

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Push-Pull Resoswitch Communication Receiver

Push-Pull Resoswitch Communication Receiver

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2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Jin, Qiutong Zheng, Kevin Nguyen, Clark T.-C. University of California Berkeley Sensor & Actuator Center Dept. of Electrical Engineering and Computer Sciences BerkeleyCA United States

ISBN: (纸本)9798350311426

A squegging-free all-mechanical communication receiver capable of receiving and demodulating up to 1 kbit/s full FSK inputs at -67.4 dBm sensitivity is demonstrated using push-pull operation of two frequency-spaced micromechanical resoswitches. Here, the use of pull-up and pull-down resoswitches is key to permitting well-controlled charging and discharging of a load capacitor and obviating the bleed resistor used in previous resoswitch receivers, reducing greatly the power draw when processing valid bits. The use of two resoswitches in concert not only permits reception of both mark and set FSK inputs to reap the benefits of full FSK reception not achieved by previous demonstrations, but also by removing squegging removes transition glitches that plagued the stability, i.e., degraded the Allan deviation, of previous resoswitch-based clock receivers. © 2023 IEEE.

关键词： bit rate FSK gold metal low power MEMS switch OOK resonator squegging zero power

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Efficient Fund Tracking system using Blockchain and GraphDB 7

Efficient Fund Tracking system using Blockchain and GraphDB

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Kaushal, Arpit Nadda, Shiveen Modi, Praveen Jaypee University of Information Technology Dept. of Computer Science And Engineering And Information Technology H.P Solan India

ISBN: (纸本)9798350371406

Powerful Asset Tracking System is a cutting edge arrangement that utilizes the force of blockchain innovation and Graph Database to give a vigorous and secure resource following stage. Conventional asset global positioning frameworks frequently face difficulties connected with straightforwardness, security and effectiveness. Notwithstanding, this framework conquers these limits by exploiting the blockchain and its decentralized and permanent nature, as well as the adaptability and versatility of diagram information bases. By incorporating blockchain innovation, the framework guarantees a straightforward and unapproved recording of asset exchanges. Every exchange is safely put away in blocks that structure a chain of information that can't be changed or erased. This increases responsiveness and disposes of the requirement for middle man, decreasing expenses and handling time. © 2023 IEEE.

关键词： Blockchain

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A Space Vector Modulation Technique for H8 Transformerless Three-Phase Photovoltaic Inverter

A Space Vector Modulation Technique for H8 Transformerless T...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Jesus, Michael A. S. T. Bahia, Filipe A. C. Tahim, Andre P. N. Pinheiro, Jose R. Costa, Fabiano F. Federal University of Bahia Salvador Brazil Federal University of Bahia Dept. of Electrical and Computer Engineering-Deec Salvador Brazil

ISBN: (纸本)9798350316445

The use of a transformerless three-phase inverter in grid-connected photovoltaic (PV) systems presents a cost-effective and compact solution. However, the absence of galvanic isolation can lead to the circulation of leakage currents, which are caused by parasitic capacitances in the PV panels. To address this issue, several topologies are proposed and combined with modulation techniques to reduce the leakage current. This paper proposes a space vector modulation technique applied to a transformerless three-phase inverter topology to further reduce the leakage current by assigning a new space vector, where the common-mode voltage will have a smaller variation in certain regions. Simulation results are presented in order to demonstrate the performance of the new modulation technique. © 2023 IEEE.

关键词： Common-mode Voltage Grid-connected Leakage Current Reduction Modulation Photovoltaic systems State Space Vector Transformerless Inverter

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Padmageetha B. G Pramod Kumar Naik Mallanagouda Patil Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bangalore India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA's interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications.

关键词： Wireless sensor networks NOMA Scalability Simulation Clustering algorithms Throughput Routing Energy efficiency Routing protocols Resource management

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition

BioPoint: Enhancing Human-Computer Interaction through Singl...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xavier Isabel Evan Campbell Loïc Olivier Gabriel Gagné Erik Scheme Ulysse Côté-Allard Gabriel Gagnon-Turcotte Dept. of Electrical and Computer Engineering Laval University Québec Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton Canada Dept. of Technology Systems University of Oslo Oslo Norway

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero." Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process’s appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG’s potential for human-computer interaction.

关键词： Human computer interaction Wrist Accuracy Pipelines Gesture recognition Rhythm Electromyography Recording Synchronization Testing

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Using Attention-Based Models to Automate Fake News Detection ∗ 10

Using Attention-Based Models to Automate Fake News Detection...

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Al-Alshaqi, Mohammed Rawat, Danda B. Howard University Dept. of Electrical Engineering and Computer Science WashingtonDC United States WashingtonDC United States

ISBN: (纸本)9798350318906

Fake news involves disseminating deceptive, offensive, and inaccurate information, masquerading as credible news. Its primary objective is to tarnish individuals' public reputations while profiting from advertisements built on false content. We introduce an efficient fake news detection system using binary classification, employing attention-based Recurrent Neural Network (RNN) models. Our unified model, characterized by superior accuracy and comprehension, undergoes rigorous evaluation with an internet-sourced dataset, consistently yielding top-tier performance metrics. Despite relying on a single model for the entire detection process, it maintains end-to-end optimization flexibility, minimizes errors, and effectively balances bias-variance trade-offs. Our study explores fake news articles, their creators, and detection challenges, identifying heterogeneous social network patterns, both overt and covert, frequently exploited by fake news creators. We introduce a Deep Diffusive Network Model incorporating network structural data into learning. Our straightforward, linear, low-latency system achieves 98% accuracy in fake news detection. In addition to modeling and detection, advanced data analysis techniques yield deeper dataset insights. © 2023 IEEE.

关键词： Recurrent neural networks

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

引用

13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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An Ameliorated Methodology for Water Supply and Management Using Binary Trees 14

An Ameliorated Methodology for Water Supply and Management U...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Setty, Vishal R Vivek, Villuri Ramesh, Sujan Sowmyashree, P. Kshama, S.B. Bms Institute of Technology and Management Computer Science and Engineering Karnataka560064 India Bms Institute of Technology and Management Computer Science and Engineering Bangalore India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

Water supply system is an infrastructure for collection, storage, transmission, and distribution of water to homes, factories, irrigation of agricultural fields, etc. Water holds an important factor in growth of the city. Earlier, lakes and rivers are the major source of water supply to the cities and villages. The rivers followed their own path throughout the city and provided water to some specific areas. Due to this the new areas which were being developed would not have direct connection to the new areas, the people would have to look for other alternatives or use the water available from the underground water beds, by building wells. Water connection to different regions is setup in an organized manner. Even though everything is organized, but there are certain drawbacks such as improper detection in the leakage of pipe lines and failure in the proper use of technology to setup and *** this paper, an attempt has been made to rectify these problems by applying the concept of binary search tree. Also, we have discussed about how pipelines can be setup underground in a smooth manner and using IOT devices to detect leakages or breakages in the pipelines, so that the water is not wasted. © 2023 IEEE.

关键词： Water supply

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