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The detection of mental health conditions by incorporating external knowledge

Journal of Intelligent Information Systems 2023年第2期61卷 497-518页

作者： Lin, Yun Sheng Tai, Liang Kuang Chen, Arbee L.P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Mental health conditions have become a growing problem;it increases the likelihood of premature death for patients, and imposes a high economic burden on the world. However, some studies have shown that if patients are detected and treated early, the social impact and economic costs of mental illness can be reduced. With the popularity of social media, people are sharing their feelings on it, which allows data from social media to be used to study mental health conditions. However, past research had been limited to the optimization of the model or using different types of data available on social media, resulting in models that only rely on data to make decisions. Moreover, people judge things not only by the data collected, but also by background knowledge. Therefore, we considered the diagnostic process of doctors and combined the knowledge of psychological screening tools and diagnostic criteria into the model. In addition, we also tested the effect of combining general knowledge. We retrieve the top m most relevant knowledge segments for each user’s post, and then put both into the prediction model. Experimental results show that our method outperforms previous studies, and the F1-score is increased more than 10% in some situations. Moreover, because the knowledge segments are automatically retrieved, our method does not require additional manual labeling, and the knowledge set can be freely adjusted. These show that our method can help detect mental health conditions and can be continuously optimized in practice. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Diseases

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Depression detection via conversation turn classification

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Multimedia Tools and Applications 2023年第25期82卷 39393-39413页

作者： Lu, Kuan-Chieh Thamrin, Syauki Aulia Chen, Arbee L. P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

The fast pace of modern life caused people to experience more pressure from their surrounding environments. As a result, depression has emerged as one of the most common diseases. To detect depression, psychiatrists need to interview patients to understand their mental health condition. However, it may not fully reflect the condition of the patient since some people cannot adequately express their feelings. As an alternative, researchers work on automated depression detection. Our work aims to improve the detection capability by extracting knowledge from psychiatric consultation. Previous studies classified the questions and answers in the interview into categories unrelated directly to depression. In this paper, we implement a novel categorization based on the Patient Health Questionnaire-8 (PHQ-8). The PHQ-8 is commonly used as a screening tool to identify the severity of depression, making it more reliable for detecting depression. Transcribed data is processed using contextual word embedding, BERT, to learn semantic information. In the final step, a transformer encoder is used to get a higher-level representation of turns. Using categorization based on PHQ-8 and BERT as word embedding, we propose a novel deep learning framework with two-level encoding for detecting depression. In the training step, we use Distress Analysis Interview Corpus (DAIC) dataset, a transcript of the interview data. The model is evaluated with different adjustments to achieve the best performance. Various experiments are designed to show the effectiveness of our method for detecting depression from the psychiatric consultation dataset. As a result, our model performs better than others using the same dataset, achieving 0.76 F1-score. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Embeddings

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A Global Medical data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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Enhancing Automated COVID Diagnosis from Chest X-rays using Convolutional Neural Networks and Transfer Learning 5

Enhancing Automated COVID Diagnosis from Chest X-rays using ...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Bukhari, Muhammad Abdullah Shah Siddiqui, Muhammad Ahmad Khalique, Sarmad Bukhari, Faisal Iqbal, Waheed University of the Punjab Department of Computer Science Lahore Pakistan University of the Punjab Department of Data Science Lahore Pakistan

ISBN: (纸本)9798350394764

We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) and deep learning-based methods. The proposed approach involves utilizing transfer learning over convolutional neural networks (CNNs) for the classification of chest X-ray images as normal or COVID-19 positive. To address the limited availability of X-ray images, we employed data augmentation techniques to expand the dataset size and enhance our model's performance on unseen data. We used variations of four pre-trained models: DenseNet, Inception, ResNet, and VGG, and found that DenseNet169 demonstrated the best performance with a validation accuracy of 100%. © 2024 IEEE.

关键词： COVID-19

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DNA Computing with Water Strider Based Vector Quantization for data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Natural Language Processing based classifier for identifying spam emails and SMS messages 3

Natural Language Processing based classifier for identifying...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Rahman, Habibur Chowdhury, Md Tanvir Sobhani, S. M. Sabbir Nabil, Mehedi Hasan Sumon, Monjurul Islam Reza, Ahmed Wasif Emon, Md Yousuf East West University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350384598

this research explores the development of a sophisticated classifier for screening spam emails and communications using Natural Language Processing (NLP). In today's digital age, where electronic communications continue to pour in, discriminating between beneficial and dangerous content is becoming increasingly important. The classifier uses NLP to analyze the intricacies of language used in Email/SMS and finds spam patterns. The classifier is trained on a dataset of both spam and non-spam messages using machine learning techniques, allowing it to reliably distinguish and categorize emails and SMS. This implementation's highest result for the Naïve Bayes Classifier is Bernoulli 0.98 percent. The goal is to improve the user experience by limiting exposure to irrelevant or potentially harmful content while assuring the seamless delivery of real communications. © 2024 IEEE.

关键词： Adversarial machine learning

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance

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SN computer science 2025年第3期6卷 1-14页

作者： Shankar, G. Geetha, M. Kalaiselvi Ezhumalai, P. Department of Computer Science and Engineering FEAT Annamalai University Chidambaram 608002 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai 601206 India

Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach utilizing the recent YOLOv9 (You Only Look Once, version 9) deep learning model to detect and monitor river water flow levels effectively. By leveraging YOLOv9’s robust object detection capabilities, the proposed system achieves precise segmentation and real-time analysis of river water regions. The method not only identifies river water areas with high accuracy but also quantifies flow levels, providing critical data for flood prediction and management. Comprehensive testing was conducted using diverse datasets representing various river conditions and flow scenarios. The results demonstrate that YOLOv9 significantly outperforms traditional methods in terms of detection speed and accuracy, making it a valuable tool for enhancing flood disaster response and management strategies. This work contributes to the development of more resilient and proactive flood disaster management systems through the integration of state-of-the-art machine learning techniques, offering significant improvements in predictive capabilities and response effectiveness. The proposed system was trained and evaluated using real-time river flood datasets, achieving a notable accuracy rate of 98%, a mean average precision (mAP) of 97.5%, precision of 98%, and recall of 98% in predicting flood level characteristics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Flood disaster management Machine learning Object detection River water flow detection Water level segmentation YOLOv9

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