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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Aswathi, K.P. Yadwad, Sunita Raj, D. Nirmal Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Pune India Pune India

ISBN: (纸本)9798331508456

As the Internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies. © 2025 IEEE.

关键词： Content based retrieval

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A framework for text mining on Twitter: a case study on joint comprehensive plan of action (JCPOA)- between 2015 and 2019

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Quality and Quantity 2022年第5期56卷 3053-3084页

作者： Behzadidoost, Rashid Hasheminezhad, Mahdieh Farshi, Mohammad Derhami, Vali Alamiyan-Harandi, Farinaz Department of Computer Science Yazd University Yazd Iran Combinatorial and Geometric Algorithms Lab. Yazd University Yazd Iran Computer Engineering Department Faculty of Engineering Yazd University Yazd Iran

In the big data era, there is a necessity for effective frameworks to collect, retrieve, and manage data. As not all tweets are hashtagged by users, retrieving them is a complicated task. To address this issue, we present a rule-based expert system classifier that uses the well-known concept of fingerprint in the judicial sciences. This expert system using defined rules first takes a fingerprint from the tweets of an emerging topic. After that, for being robust the fingerprint, using a rule-based search, the fingerprint with its neighbor features is to be updated. For detecting the unhashtagged tweets of the topic, each tweet in question checks itself with the generated fingerprint. By using the Twitter APIs of Streaming API and REST API, there is no way to access old Twitter data. To address this issue, we present a hybrid approach of Web scraping and Twitter streaming API. When the presented framework is compared to other similar works, there are (1) a novel two-class classification using an expert system approach that can intelligently and robustly detect the most of tweets of the emerging topics although they do not have the hashtag of the topic.;(2) a practical method for extracting old Twitter data. Also, we made a comparative text mining in 195649 collected Persian and English tweets about JCPOA. The JCPOA is one of the most important international treaties about the nuclear program between the Islamic Republic of Iran and the USA, China, France, Russia, Germany, and England. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Fingerprint Iran deal JCPOA Sentiment analysis Text mining Topic detection Twitter

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Detection of Fetal Cardiac Chamber Three Vessel Trachea View using Deep Learning 9

Detection of Fetal Cardiac Chamber Three Vessel Trachea View...

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9th International Conference on Electrical engineering, computer science and Informatics, EECSI 2022

作者： Sapitri, Ade Iriani Nurmaini, Siti Rini, Dian Palupi Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Gusendi, Arjuno Universitas Sriwijaya Doctoral in Engineering Sciences Faculty of Engineering Indonesia Universitas Sriwijaya Intelligent System Research Group Faculty of Computer Science Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

In fetal cardiology, echocardiographic imaging has been shown to aid in the diagnosis of fetal monitoring for the development of fetal heart function. The small heart size and the lack of expertise in fetal echocardiography of some sonography pose challenges in terms of research. The use of technology to enhance the image assists in the diagnosis of abnormalities important for optimal fetal cardiac assessment. Multi object detection is the most challenging but very informative task in basic machine learning. We must detect cardiac substructure precisely the condition of the fetal heart, especially in normal conditions. Knowing the normal heart substructure can provide information about the presence or absence of fetal heart abnormalities. Multiple ways to view fetal heart chambers, four chambers (4CH) basic standard cardiac scan, left and right ventricular outflow tract (LVOT/RVOT) focusing on structural pathways, and Three Tracheal Vessels (3VV/3VT) complex fetal heart scan confirms flow in the three blood vessels helps determine whether aortic arch problems are causing normal heart conditions or not. The ductus arteriosus (DUCT), ascending aorta (AoA), superior vena cava (SVC), and trachea. In particular, the framework performs detection using a deep learning detection model applied to 3VT ultrasound images. This study identifies the evaluation using the mean average precision (mAP). The results of the study detecting the normal condition of the fetal heart substructure gave good results on the results of the mAP evaluation of 87.55% and each object of DUCT, AoA, SVC, Trachea with AP values of 92%, 91%, 91%, 76%. © 2022 Institute of Advanced engineering and science (IAES).

关键词： Deep Learning Detection Multi Object Ultrasound Image

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Trajectory-based fast ball detection and tracking for an autonomous industrial robot system

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International Journal of Intelligent Systems Technologies and Applications 2021年第2期20卷 126-145页

作者： AbdElKhalek, Youssef M. Awad, Mohammed Ibrahim Abd El Munim, Hossam E. Maged, Shady A. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechatronics Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Galala University Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Autonomising industrial robots is the main goal in this paper;imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Object detection

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Real-Time Multi-Class Infection Classification for Respiratory Diseases

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computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed El.Shafee Walid El-Shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized *** disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and *** prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected *** need for auxiliary computer-aided diagnostic(CAD)systems has *** recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 *** advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible *** research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona *** purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better *** is designed and implemented to improve the generalization ability of the classifier for binary and multi-class *** with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color *** proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic *** can also be used to forecast other lung-related *** the system can assist medical practitioner

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Nevien A. Mahdy Ahmed F. Elnokrashy D. A. Hammad Wael A. Mohamed Electrical Engineering Department Benha Faculty of Engineering Benha University Benha Egypt Computer Science Department Faculty of Information Technology and Computer Science Nile University Giza Egypt Basic Engineering Sciences Department Benha Faculty of Engineering Benha University Benha Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D.

关键词： Training Wearable Health Monitoring Systems Real-time systems Data models Glucose Diabetes Insulin

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An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

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computers, Materials & Continua 2021年第12期69卷 3889-3902页

作者： Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Mohammed A.Al-Hagery Huda G.Iskander Quang Ngoc Nguyen Babar Shah Ki-Il Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Communications and Computer Engineering Faculty of Science and EngineeringWaseda UniversityShinjuku-kuTokyo169-0051Japan College of Technological Innovation Zayed UniversityAbu DhabiUAE Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

The Internet of Things(IoT)is the fourth technological revolution in the global information industry after computers,the Internet,and mobile communication *** combines radio-frequency identification devices,infrared sensors,global positioning systems,and various other *** sensing equipment is connected via the Internet,thus forming a vast *** these physical devices are connected to the Internet,the user terminal can be extended and expanded to exchange information,communicate with anything,and carry out identification,positioning,tracking,monitoring,and triggering of corresponding events on each device in the *** real life,the IoT has a wide range of applications,covering many fields,such as smart homes,smart logistics,fine agriculture and animal husbandry,national defense,and *** of the most significant factors in wireless channels is interference,which degrades the system *** the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity,it does not solve the problem of poor detection ***,this study proposes a maximumlikelihood-based QR decomposition *** main idea is to estimate the initial level of detection using the maximum likelihood principle,and then the other layer is detected using a reliable *** optimal candidate is selected from the feedback by deploying the candidate points in an unreliable *** results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature.

关键词： 6G networks internet of things resource allocation optimization

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein

Multimodal biometric authentication based on deep fusion of ...

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作者： El-Rahiem, Basma Abd El-Samie, Fathi E. Abd Amin, Mohamed Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

Biometric identification depends on the statistical analysis of the unique physical and behavioral characteristics of individuals. However, a unimodal biometric system is susceptible to different attacks such as spoof attacks. To overcome these limitations, we propose a multimodal biometric authentication system based on deep fusion of electrocardiogram (ECG) and finger vein. The proposed system has three main components, which are biometric pre-processing, deep feature extraction, and authentication. During the pre-processing, normalization and filtering techniques are adapted for each biometric. In the feature extraction process, the features are extracted using a proposed deep Convolutional Neural Network (CNN) model. Then, the authentication process is performed on the extracted features using five well-known machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN). In addition, to represent the deep features in a low-dimensional feature space and speed up the authentication task, we adopt Multi-Canonical Correlation Analysis (MCCA). We combine the two biometric systems based on ECG and finger vein into a single multimodal biometric system using feature and score fusion. The performance of the proposed system is tested on two finger vein (TW finger vein and VeinPolyU finger vein) databases and two ECG (MWM-HIT and ECG-ID) databases. Experimental results reveal improvement in terms of authentication performance with Equal Error Rates (EERs) of 0.12% and 1.40% using feature fusion and score fusion, respectively. Furthermore, the authentication with the proposed multimodal system using MCCA feature fusion with a KNN classifier shows an increase of accuracy by an average of 10% compared with those of other machine learning algorithms. Therefore, the proposed biometric system is effective in performing secure authentication and assisting the stakeholders in making

关键词： Authentication

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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