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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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Effective Image Representation using Double Colour Histogram for Content-Based Image Retrieval

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Informatica (Slovenia) 2021年第7期45卷 97-105页

作者： Martey, Ezekiel Mensah Lei, Hang Li, Xiaoyu Appiah, Obed School of Information & Software Engineering University of Electronic Science and Technology of China China Department of Computer Science and Informatics University of Energy and Natural Resource Ghana

Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Image representation

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A Machine Learning Approach for the Underwater Investigation of Malicious Activity in a Wireless Sensor Network

A Machine Learning Approach for the Underwater Investigation...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Murali Dhar, M.S. Mahaveerakannan, R. Ravindaran, F. Murthyraju, Kommisetti Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Tamil Nadu Coimbatore India Shri Vishnu Engineering College for Women Department of Electronics and Communication Engineering Andha Pradesh Bhimavaram India

ISBN: (纸本)9798350313987

Underwater Wireless Sensor Networks (UWSN) have drawn increased interest from researchers in recent years due to advancements in underwater tracking, the implementation of multiple uses, and marine monitoring. Both researchers and business are drawn to the UWSN field. It is also susceptible to hostile attacks and threats because of the harsh undersea surroundings, its own capabilities, and the open acoustic channel. These features make it simple for attackers to intercept information across the source and destination. Many studies have been done to secure UWSN from malicious attacks, but the majority of them rely on a threshold that is determined before deployment or a training set of data. Because there is no proven security foundation, it is difficult and challenging for UWSN to identify harmful attacks. To identify harmful attacks in a UWSN, we support vector machines in this research. SVM produces good results and requires significantly less time to train than neural networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues. © 2023 IEEE.

关键词： malicious attacks security support vector machine underwater wireless sensor network (UWSN)

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in engineering & sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Editorial

Learning and Analytics in Intelligent Systems

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Learning and Analytics in Intelligent Systems 2025年 47卷 vii-x页

作者： Tsihrintzis, George A. Favorskaya, Margarita N. Kountcheva, Roumiana Patnaik, Srikanta Department of Informatics University of Piraeus Piraeus Greece Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia TK Engineering Sofia Bulgaria Interscience Institute of Management and Technology Odisha Bhubaneswar India

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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Computer Systems science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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A Fused Machine Learning Approach for Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Sarcasm detection in product reviews using textual entailment approach 23

Sarcasm detection in product reviews using textual entailmen...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Shubham Sinha Tarun Vijeta Pratik Kishor Kubde Ayush Pradeep Gajbhiye Mansi Anup Radke Christopher Jones Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India School of Computer Science and Informatics Cardiff University UK

ISBN: (纸本)9798400709227

Sarcasm is a form of sentiment characterized by the use of words that express the opposite of what is meant. Sarcasm detection has applications in multiple domains ranging from sentiment analysis in product reviews to user feedback, and online forums. Sarcasm detection is important to understand user opinions and intentions in areas such as sentiment-based classification and opinion mining. This can result in better product development and customer service. Sarcasm detection can be a challenging task because sarcastic sentences may use positive expressions to convey negative meanings or may use negative sentences to convey positive meanings. Also, sarcastic sentences form a very small component of the entire communication. The increasing use of sarcasm in various social media such as Twitter, Reddit, Amazon product reviews, etc. has highlighted the importance of detecting and understanding sarcasm in various contexts. Sarcasm detection is a challenging problem for NLP systems that often rely on statistical models for performing sentiment analysis. In this research, the focus is on the use of a textual entailment approach for detecting sarcasm. Textual entailment is a natural language inference task that involves determining whether one text (hypothesis) can be derived from another text (premise). The underlying assumption behind this approach is that - if there is a contradiction between the premise and hypothesis, we can say that the hypothesis is sarcastic. To test our approach, an annotated corpus of 3000 product reviews was developed methodically from the Amazon Reviews dataset and tested using the textual entailment approach. The proposed approach achieved an F1 score of 0.76 on this dataset. The result is better than the baseline considered which is the BERT binary classifier which gives an F1 score of 0.48 on the same dataset.

关键词： Hypothesis Natural Language Inference Premise Sarcasm Detection Textual Entailment

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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