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Generation of High-Quality Relevant Judgments through Document Similarity and Document Pooling for the Evaluation of Information Retrieval systems 14

Generation of High-Quality Relevant Judgments through Docume...

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14th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2022

作者： Joseph, Minnu Helen Ravana, Sri Devi University of Malaya Faculty of Computer Science & Information Technology Department of Information Systems Malaysia Faculty of Computer Science Engineering & Technology Malaysia

ISBN: (纸本)9781665493345

The Information Retrieval System Evaluation have carried out through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of document corpus, topics, and a set of relevance judgements. The relevant judgements are the documents which retrieved from the test collections based on the topics. The precision of the evaluation process is based on the number of relevant documents in the relevant judgement list called qrels. This paper presents a study on how methodologies like pooling and document similarity helps to generate more relevant documents into the relevance judgments set in order to increase the accuracy of the evaluation process. The initial results have shown that combination of pooling with document similarity performs better compared to base clustering or classification. © 2022 IEEE.

关键词： Information retrieval systems

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Linear Delta Kinematics Feedrate Planning for NURBS Toolpaths Implemented in a Real-Time Linux Control System 1

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19th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2022

作者： Karasek, Gabriel Erwinski, Krystian Department of Automatics and Measurement Systems Institute of Engineering and Technology Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun Torun Poland

ISBN: (数字)9783031483035

ISBN: (纸本)9783031483028

This paper presents an advanced concept of a computationally efficient feedrate planning algorithm for parallel kinematics machine in a linear delta configuration. The algorithm is developed for toolpaths defined as Non-Uniform Rational B-Splines (NURBS). The proposed method is developed to generate feedrate profiles subject to velocity, acceleration and jerk limitations. It is shown that limiting the cartesian parameter also significantly limits the violations of actual joint velocities, accelerations and jerks. The algorithm is implemented in a real-time Linux based control system and its real-time performance is ascertained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Acceleration

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Simões, Miguel Pereira, Tania Silva, Francisco MacHado, José Carlos Oliveira, Hélder P. University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal University of Porto Ipatimup - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde Fmup - Faculty of Medicine Porto Portugal

ISBN: (纸本)9798350337488

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability. © 2023 IEEE.

关键词： Artificial Neural Networks Cancer Immunotherapy Microsatellite Instability Prediction RNA-seq Data

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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Optimal Time Splitting in Wireless Energy Harvesting-Enabled Sensor Networks 4th

Optimal Time Splitting in Wireless Energy Harvesting-Enable...

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4th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Bepari, Dipen Mondal, Soumen Pareek, Prakash Gupta, Nishu Department of Electronics and Communication Engineering National Institute of Technology Chhattisgarh Raipur492010 India Department of Electronics and Communication Engineering National Institute of Technology WB Durgapur India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway

ISBN: (纸本)9783031488900

This research paper presents a study on wireless energy harvesting (WEH) protocols and their impact on the performance of sensor networks. A time switching (TS)-based WEH protocol is proposed, which allows sensor nodes to switch between energy harvesting and data transmission modes. The primary objective of this research is to maximize the uplink (UL) sum throughput while considering the constraint of a minimum downlink (DL) throughput. To achieve this, an optimization problem is formulated, and the Karush-Kuhn-Tucker (KKT) conditions and Lagrangian multiplier are employed to solve the optimization problem. Additionally, a UL-DL channel gain-based unequal sensor node operating time scheme is introduced. The results of the study demonstrate that increasing the DL threshold data rate enhances UL performance in terms of sum throughput and outage. Moreover, the proposed channel gain-based unequal operating time scheme outperforms the equal sensor node operating time approach. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy harvesting

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Power Planning in Hydropower Plants Using Artificial Intelligence: A Case Study of Nigeria

Power Planning in Hydropower Plants Using Artificial Intelli...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Nwobi-Okoye, Chidozie Chukwuemeka Takim, Stephen A. Okiy, Stanley Chukwuemeka Odumegwu Ojukwu University Engineering Analysis and Systems Engineering Unit Faculty of Engineering Uli Nigeria Cross River State University Department of Mechanical Engineering Uli Nigeria Petroleum Training Institute Department of Welding and Offshore Technology Effurun Nigeria

ISBN: (纸本)9798350389388

Power generation in Nigeria has faced significant challenges, prompting the urgent need for improved efficiency and performance in existing hydropower plants. This research aims to address this need by developing Artificial Intelligence (AI) models to facilitate power planning in three operational hydropower plants in Nigeria. These AI models, specifically artificial neural network (ANN) models trained on historical data from the power plants, were utilized to forecast inflows and energy generation. The results demonstrate the effectiveness of the AI models, with the best inflow forecast model achieving correlation coefficients (r) of 0.8825, 0.8785, and 0.8712 for Jebba, Kainji, and Shiroro hydropower plants respectively. Similarly, the energy generation forecast model achieved high r values of 0.9690, 0.9732, and 0.9643 for the respective plants. These models offer invaluable support to power plants in planning and scheduling operations. However, the study encountered challenges, primarily related to the quality and availability of data. Specifically, the absence of weather data posed a limitation in developing more accurate prediction models for inflow. Despite these challenges, the AI models present a significant advancement in enhancing the efficiency and performance of hydropower plants in Nigeria. © 2024 IEEE.

关键词： Power distribution planning

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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Computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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An Evaluation of Integrating ERP System to Develop a Strategy Business 8

An Evaluation of Integrating ERP System to Develop a Strateg...

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Wijaya, Santo Fernandi Wiratama, Jansen Kuswanto, Verri School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Information Systems Department Tangerang11480 Indonesia Buddhi Dharma University Faculty of Science and Technology Information Systems Department Tangerang11480 Indonesia

ISBN: (纸本)9798350326093

The digital changes business patterns that require companies by using an integrated system for strategic decision-making. ERP system is one technological innovation that enable to have a competitive advantage. But the fact is that only a few companies have succeeded in implementing the ERP system. The objective of this research is to integrate the ERP system with business strategy in implementing ERP systems. The research focuses on developing an ERP system integration model for implementing the SAP ERP success. The methodology is based on previous research for the ERP implementation. This research examines quantitative data to determine indicators using entropy and conducts case studies in the industry. The research results prove that the management support indicator is an essential factor in integrating ERP systems to develop the company's strategic business. The output research is the design of the integration model development of the ERP system. © 2023 IEEE.

关键词： Decision making

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Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Utilizing Genetic Programming to Enhance Polygenic Risk Scor...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hurta, Martin Schwarzerova, Jana Naegele, Thomas Weckwerth, Wolfram Provaznik, Valentine Sekanina, Lukas Brno University of Technology Faculty of Information Technology Department of Computer Systems Brno Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Department of Biomedical Engineering Brno Czech Republic Vienna Austria Lmu Munich Plant Evolutionary Cell Biology Faculty of Biology Munich Germany Vienna Austria

ISBN: (纸本)9798350337488

The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies. © 2023 IEEE.

关键词： Computational biology Genetic Programming Genetic Variations Polygenic risk score

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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