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Prediction of Chronic Diseases Using Parallel 1D-CNN Feature Extraction and SVM Classification Based on SMOTE 4th

Prediction of Chronic Diseases Using Parallel 1D-CNN Feature...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mesbah, Fatma Zohra Tassadit Ait Abidine, M'hamed Bilal Fergani, Belkacem Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn BP 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9783031821523;9783031821530

Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector machine (SVM) technique is applied for CD classification. the uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. this strategy manages class imbalance in CD databases. the suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machine learning methods and other existing state-of-the-art models. the effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes database (PIDD) and the Cleveland Heart Disease database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.

关键词： Chronic diseases data classification SMOTE Parallel unidimensional convolutional neural network (1D-PCNN) SVM

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in data Science, ICCIDS 2023

Proceedings of the 6th IFIP TC 12 International Conference o...

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in data Science, ICCIDS 2023

ISBN: (纸本)9783031382956

the proceedings contain 24 papers. the special focus in this conference is on Computational Intelligence in data Science. the topics include: the Use of the Semantic Network for Categorization of Problems Solved by Design thinking;Cloud Based Remote File Access from PC to Mobile Using File Directory;stock Trade Recommendations Using Knowledge Graph;understanding the Determinants of Adoption of Healthcare Information Technology;static Clustering Centralized Multi-hop Routing Protocol Based on Fuzzy Logic with Fish Swarm Intelligence;computer Vision Aided machine learning Framework for Detection and Analysis of Arm Flapping Stereotypic Behavior Exhibited by the Autistic Child;social Media Hate Speech Detection Using machine learning Approach;frontotemporal Dementia Detection Model Based on Explainable machine learning Approach;AJNA - Voice Assisted Captioning Tool for the Blind;Expert System for Real Time Arm Movement recognition Based on Surface EMG Signal;extraction of Vocal Tract Length from Formant Frequencies for Forensic Speech Applications in Noisy Environment;Smart Parking System with Automated Vehicle Log Using Haar Cascade Classifier ANPR;gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination;cataract Fundus Image Detection Using Hybrid Deep learning Model;enhanced Image Caption Generator Using Deep learning;edge Detection and Huffman Encoding Based Image Steganography with High data Embedding Capacity;DAPP IT – Multipurpose ID Card System for Universities;local Vegetable Freshness Classification Based on Chronological Monitoring Using Transfer learning Approach;analysis of Cost-Sensitive Algorithms for Degree of Imbalancing;performance Evaluation of Several Transfer learning Models for Classification of Road Surface State;Secure Image Encryption Model for Cloud-Based Healthcare Storage Using Hyperchaos and DNA Encoding.

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Intelligent Ensemble learning Approach for Intrusion Detection System in IoMT Environment 6th

Intelligent Ensemble Learning Approach for Intrusion Detecti...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Priyadarshani, Pooja Puspita Dash, Pandit Byomakesha University Odisha Baripada India Andhra Pradesh Srikakulam India

ISBN: (纸本)9789819780891

the influence of technology on our society is significant and ubiquitous in our daily lives. the impact of artificial intelligence (AI) is seen in several domains, such as robots, education, space exploration, business, and healthcare. Due to technological advancements, information may now be rapidly accessible via devices such as wearables, social networking platforms, telephones, and collaborative tools. It has revolutionized networking, travel, healthcare, and communication. the internet of medical things (IoMT) is an emerging field in healthcare that utilizes advancements in the internet of things (IoT). However, the use of technology in medical devices raises concerns around security and data privacy. Healthcare networks are susceptible to several types of attacks, such as brute-force attacks, floods, distributed denial of service (DDoS) attacks, port scanning, and web crawling. these vulnerabilities arise from resource constraints and open connections. this chapter utilizes machine learning (ML) methodologies to enhance the security of healthcare systems and increase the accuracy of intrusion detection systems (IDSs) in addressing these concerns. the study demonstrates that the ensemble learning (EL)-based model known as adaptive boosting (AdaBoost) surpasses other approaches, achieving an exceptional accuracy rate of 99.89% compared to all other models analyzed. Significantly, Bagging and Random Forest (RF) achieve accuracy rates of 99.85% and 99.82%, respectively, demonstrating exceptional precision. the study findings indicate that the suggested IDS works better than other comparable models, as shown by the investigations conducted on the multi-step cyber-attack dataset (MSCAD) dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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Chinese named entity recognition of transformer bushing fault based on BiLSTM-CRF 6

Chinese named entity recognition of transformer bushing faul...

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6th IEEE international Electrical and Energy conference, CIEEC 2023

作者： Zhang, Yufang Yuan, Zhikang Gao, Shuojie College of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798350346671

Bushing is an important part of transformer, which is often in the environment of high voltage and high current, and is prone to failure. the vast majority of transformer failures are caused by bushing failures, which will bring about serious economic losses. With the development of information technology, the power system is also moving towards digitalization. Knowledge graph plays an important role in the rapid processing of power information. For this technology, named entity recognition is the key step to build knowledge graph, which can extract power information entities and promote the process of power system digitalization. therefore, this paper proposes a transformer bushing fault extraction method based on Chinese named entity recognition, and extracts the fault information from the bushing fault text based on the BiLSTM-CRF natural language processing model. First of all, according to the characteristics of transformer bushing fault text, the schema layer is constructed using the top-down entity extraction method, and its data layer is also constructed according to the attributes defined in the schema layer;then, the mainstream machine learning models are compared with the model proposed in this paper, and the effectiveness of this method is evaluated by using precision, recall, F1 score, which reaches 92.91%, 91.89% and 92.40% respectively. © 2023 IEEE.

关键词： Deep learning

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Secure Communication via IRIS and PALM Print Validation using Deep BayesianNet 6

Secure Communication via IRIS and PALM Print Validation usin...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Moorthi, M. Kirupa Sankari, K.E. Kanimozhi, G.K. Pooja, A. HOD-BME&Medical Elecctronics Saveetha Engineering College Chennai India BME Saveetha Engineering College Chennai India

ISBN: (纸本)9798350398496

More and more sensitive information is being moved around the internet, so it is imperative that it be protected during transit. this is especially true of user-enabled private interactions. When it comes to data protection, a biometric security system's ability to use an individual's distinct biological characteristics is unrivalled. In this manner, multi-modal biometric generates more interest than a single biometric identification system. Researchers are interested in multimodal biometric systems' certificates because, compared to those of a unimodal biometric system, they offer advantages like improved accuracy in recognition competence and dependable security. Here, a Hand vein and Iris based multimodal Fingerprint system is designed with Deep learning. Encrypting the alignment maintaining characteristics, the hand vein based method aims to be more robustly tolerant of the possible fractures, rotating, and translational shifts. As a step towards Palm Vein Identification, a picture processing method is used to examine the vascular pattern of the hand palm vein. Iris biometrics were integrated into the authentication process to further strengthen its reliability. An infrared camera or scanner device is used to collect the palm vein pattern, and this data is then put through four distinct filters. Deep Bayesian Net was used for categorization in the suggested system. Accuracy and discovery time are used as indicators of the system's effectiveness. © 2023 IEEE.

关键词： Deep learning

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PRNU-Net: a Deep learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone 26

PRNU-Net: a Deep Learning Approach for Source Camera Model I...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Qatar Univ Dept Comp Sci & Engn Doha Qatar Northumbria Univ Dept Comp & Informat Sci London England Univ Sharjah Ctr Data Analyt & Cybernet Sharjah U Arab Emirates

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and machine learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. the experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

关键词： Deep learning Video on demand Social networking (online) databases Feature extraction Cameras Propagation losses

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. the Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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A Novel Graph Representation learning Approach for Visual Modeling Using Neural Combinatorial Optimization 10th

A Novel Graph Representation Learning Approach for Visual Mo...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Chatterjee, Subhrasankar Pain, Subrata Samanta, Debasis Indian Inst Technol Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

the human visual system is a complex network of neurons that employs robust mechanisms to perceive and interpret the environment. Despite significant advancements in computer vision technologies in recent years, they still need to improve compared to human abilities, particularly in recognizing faces and interpreting scenes. As a result, there is a growing interest in understanding the underlying mechanisms of human vision. Artificial Intelligence (AI) systems, specifically computer vision models, represent the assigned task using a learned or systematically generated vector space known as the Latent Space. However, the field of brain representation space remains relatively less explored. Despite significant progress, a research gap exists in generating an optimal representation space for human visual processing. While graph-based representations have been proposed to better capture inter-region relationships in visual processing, learning an optimal graph representation from limited data remains a challenge, especially when there is no ground truth. Due to the lack of labeled data, supervised learning approaches are less preferred. the present study introduces a novel method for graph-based representation of the human visual processing system, utilizing Neural Combinatorial Optimization(NCO). We have obtained an accuracy of 60% from our proposed framework, which is comparable to other methods for eight class classification in Visual Modelling.

关键词： Visual Modeling Neural Combinatorial Optimization AutoEncoder

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Research on recognition of Precursor Anomaly data Based on Improved Reverse Selection Algorithm 6

Research on Recognition of Precursor Anomaly Data Based on I...

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6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Qin, Le Zhang, Yuerong Chen, Lili Chen, Jun Zhang, Bo School of Information and Architecture Engineering Anhui Open University Anhui Hefei230022 China Anhui Seismological Bureau Anhui Seismological Observatory Anhui Hefei230000 China Chuzhou Seismological Bureau Chuzhou Seismological Monitoring Department Anhui Chuzhou239000 China

ISBN: (纸本)9781837242672

In order to solve the problems of low accuracy of seismic precursor anomaly data detection method and long-term reliance on manual observation and calculation to identify precursor anomalous data, this paper proposes a precursor anomaly data identification method with improved Reverse Selection Algorithm, which uses the seismic precursor data provided by the China Earthquake Network as the input index, and uses the particle swarm optimization PSO optimization Reverse Selection Algorithm to train the dimensionally reduced data, so as to achieve the purpose of identifying abnormal data. It solves the problem that the traditional Reverse Selection Algorithm can only be set artificially and cannot independently determine the self-radius Rs, and the algorithm anomaly recognition rate is improved. Compared with other anomaly data detection algorithms such as traditional Reverse Selection Algorithm and support vector machine algorithm, the running speed, global search ability, stability of operation, and prediction rate of abnormal data recognition rate of the system are improved. © the Institution of Engineering & Technology 2024.

关键词： Consensus algorithm

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data Pre-processing Solution Using Statistical and data mining Techniques 6th

Data Pre-processing Solution Using Statistical and Data Mini...

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6th international conference on Information Systems and Management Science, ISMS 2023

作者： Deshkar, Prarthana A. Laghate, Koustubh Ghorpade, Aditya Padole, Divyam Shende, Harsh Kawale, Piyush Sakhare, Piyush Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Incredo Technologies Nagpur India Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9783031707889

the research explores comprehensive strategies for handling missing data, encompassing statistical imputation methods and the K-Nearest Neighbors (KNN) algorithm. Various statistical techniques, such as mean, median, mode imputation, and machine learning-based approaches, are evaluated, emphasizing their impact on data quality. Additionally, the study delves into outlier detection using K-means clustering, contributing insights into the identification and handling of anomalies. the proposed utility integrates user-friendly interfaces, facilitating effective data preprocessing and analysis. the KNN algorithm, with Euclidean and NaN Euclidean distance metrics, is employed for missing value imputation, enhancing dataset completeness. the investigation concludes with considerations on the significance of Root Mean Squared Error in evaluating model performance. Overall, the research addresses critical aspects of data preprocessing, offering practical insights for researchers and practitioners. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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