EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology is open access, a peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining, knowledge management, IT applications to the needs of patients, disease prevention, and awareness, electronic and mobile health platforms including design and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2023, the journal follows a continuous publication model. INDEXING: Scopus (CiteScore: 3.3), Compendex, DOAJ, ProQuest, EBSCO, DBLP
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
IoMT and Data Privacy in Alzheimer’s Care for Older Adults: A Systematic Review
INTRODUCTION: The use of medical Internet of Things (IoT) devices becomes essential in everyday healthcare routines as the older adults confront an increasing risk of cyber victimization because of Alzheimer's illness. Despite the myriad benefits of IoT devices, escalating concerns about data privacy and cybersecurity loom larger, given the cognitive and physical decline associated with Alzheimer's. OBJECTIVES: Focusing on the challenges faced by older adults, especially those with Alzheimer's, this study aims to investigate the data privacy and security risks and advocates for specialized education and user-friendly interfaces to narrow the digital divide. METHODS: Eighteen peer-reviewed articles were selected based on predefined inclusion criteria, focusing on older adults or Alzheimer's patients, IoMT in healthcare, and data privacy or security concerns. Searches were conducted in PubMed and IEEE Xplore, following PRISMA 2020 guidelines. A structured data extraction matrix informed by Grounded Theory was used to chart and analyze key themes across selected studies, including types of vulnerabilities, consequences, and proposed solutions. RESULTS: Recurring vulnerabilities included social engineering, data breaches, weak authentication, and poor access control. Effective mitigation strategies identified include patient and caregiver education, improved informed consent procedures, robust encryption, and data governance reforms. CONCLUSION: Smart home technology and the digitization of the healthcare field have shown great promise in caring for Alzheimer's patients, positively affecting their ability to live on their own safely. They also create new challenges with the need to protect the sensitive information they gather. Our research emphasizes developing strategies that can create awareness and educate patients and their caregivers to further reduce the risks related to data security.
Copyright (c) 2024 Fahim Islam Anik, Md Mehedi Hasan, Juan Rodriguez-Cardenas, Richard Beiswanger, Masrura Tasnim, Mary Ramos, Nazmus Sakib https://creativecommons.org/licenses/by-nc-sa/4.0
Study Results of mCARE: Developing, Deploying, and Analysing the End-to-End Results of a Mobile-Based Remote Monitoring Tool for Children with Autism Spectrum Disorder in Bangladesh
Mental health is one of the most neglected healthcare issues in developing countries. The situation is worse in the Global South due to stigma, superstitions, and many other social-cultural-financial constraints. Lack of mental health professionals and regular monitoring has deprived families raising children with Autism Spectrum Disorder (ASD) of the desired care and support. However, the overwhelming adoption of mobile phones in Bangladesh has created an unprecedented opportunity to overcome these various constraints. To leverage this opportunity, we designed, developed, and evaluated mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method), a mobile application that integrates the Experience Sampling Method (ESM) with the local healthcare practice by putting the caregivers in the loop. mCARE collected behavioral and developmental progress parameter values from the caregivers of 300 children with ASD periodically (daily/ weekly/ biweekly/ monthly) along with sociodemographic data of the family. This paper reports: (a) the context and challenge identification phases that validate the requirement of a mobile based tool; (b) evolution of mCARE following Value Sensitive Design; (c) short term and long-term impact analysis (qualitative and quantitative) of mCARE, and; (d) broader implications of these findings for the HCI scholarship along with the impact of mCARE during COVID-19.
Copyright (c) 2024 Md M. Haque, Md Ishrak Islam, Masud Rabbani, Dipranjan Das, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Shaheen Akhter, Shahana Parveen, Azima Begum, Mohammad Shaha A Patwary, Austin Schmidt, Brandon Franczak, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed https://creativecommons.org/licenses/by-nc-sa/4.0
Exploring Diverse Features: A Thorough Survey for Anxiety Disorders
INTRODUCTION: The prevalence of mental health issues, mainly anxiety disorders, has risen significantly in today’s fast paced world. Thus, giving the imperative to confront the challenges associated in identifying these issues. OBJECTIVE: The objective of this review paper is to signify the importance of different features or parameters (acoustic, prosodic, linguistic, facial, neuroimaging, psychological, and physiological) being extracted from different data modalities (audio, video, psychological, physiological, textual, and neuroimaging) that are being used to assess different kinds of mental disorders. METHODS: Considering the systematic literature review technique, a total of 102 studies have been identified in the field of anxiety disorders and mental health issues spanning the years 2015 to 2024. By considering diverse features being used to diagnose different kinds of anxiety disorders, this paper provides a foundation for future research that will help researchers to design the new strategies and techniques to handle the anxiety disorder. CONCLUSION: This comprehensive review paper outlines the details of diverse features extracted across various data modalities, contributing significantly to the prediction of a wide range of anxiety disorders.
Copyright (c) 2024 Rakhi Nagpal, Saravjeet Singh, Aditi Moudgil https://creativecommons.org/licenses/by-nc-sa/4.0
Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens
INTRODUCTION: Co-design has become a fundamental pillar of formative digital health research. Typically, this approach involves in–person workshops that involve a rich but limited amount of data. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. This is a novel, exploratory approach in mHealth design that may appease common health research concerns surrounding reliability and validity, whilst providing swifter feedback to meet product development timelines. OBJECTIVES: The objective of this exploratory single case study was to explore the virtual, crowdsourced, co-design of Sleep Solved, an educational mHealth sleep app designed with teens. In doing so, we wished to learn which virtual methods were used to engage teens in the co-design and to explore how these virtual co-design methods can be adapted for large-scale ideation and testing. METHODS: We conducted an enquiry-based iterative case study utilising the Bayazit 3-stage model. 85 teens participated over 11 months. Data was thematically analysed over several design iterations. RESULTS: Rapid virtual feedback allowed for quick pivots in a short time frame. Four stages of feedback from teens led to iterative changes to scientific information contextualisation and user experience, from lo-fidelity mock-ups through to a coded app beta. CONCLUSION: The co-design of Sleep Solved exemplified the potential of virtually crowdsourcing teens in mHealth. Key to this evolution will be the ability to leverage big data utilising AI and machine learning approaches to data collation and synthesization, such that meaningful and contextual findings can be applied in line with software development timelines.
Copyright (c) 2024 Anthony Duffy, Sarah E Bennett, Lucy Yardley, Sylvain Moreno https://creativecommons.org/licenses/by-nc-sa/4.0
Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders
INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition that can develop after exposure to traumatic events, often resulting in symptoms that severely impair daily functioning. Current diagnostic methods largely rely on subjective assessments, highlighting the need for objective, non-invasive tools to improve diagnostic precision. OBJECTIVES: This study aims to develop and validate an innovative deep learning approach using autoencoder neural networks to detect PTSD through analysis of electrocardiography (ECG) signals. The goal is to provide a reliable and sophisticated diagnostic method that bridges computational and clinical domains. METHODS: We employed autoencoder neural networks to analyze ECG data collected from wearable heart zone sensors. This unsupervised learning model was trained to detect subtle anomalies in the ECG signals that may serve as biomarkers for PTSD. The methodology was evaluated using data collected from individuals with and without PTSD symptoms. RESULTS: The proposed model demonstrated strong potential as an objective diagnostic tool, successfully identifying patterns in ECG signals associated with PTSD. The analysis confirmed the model’s ability to distinguish PTSD-related anomalies with 83% accuracy. CONCLUSION: This research introduces a novel, non-invasive diagnostic methodology for PTSD using deep learning and wearable ECG data. The findings support the model's value as a potential objective biomarker, contributing to more precise psychiatric diagnostics and expanding the role of machine learning in healthcare.
Copyright (c) 2024 Vasileios Skaramagkas, Ioannis Kyprakis, Georgia Karanasiou, Dimitrios Fotiadis, Manolis Tsiknakis https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning
Human Gait Analysis is crucial in healthcare applications, with numerous research works focusing on machine learning and deep learning approaches for tasks such as abnormal gait detection and gait quality assessment. However, developing such models requires collecting and sharing a significant amount of patient data, raising privacy concerns. In this study, we introduce the world’s first technique for constructing a deep neural network model to stratify patients’ pain levels based on video recordings of timed up-and-go activities, while ensuring privacy preservation through modern federated learning algorithms. Our experimental results demonstrate the effectiveness of this technique in accurately stratifying LBP levels without the need for data sharing among local clients to maintain privacy.
Copyright (c) 2024 Liyun Gong, Miao Yu, Saeid Pourroostaei https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning
Gait analysis plays a pivotal role in diagnosing a spectrum of neurological and musculoskeletal disorders. Variations in gait patterns often serve as early indicators of underlying health conditions, underscoring the importance of precise and timely analysis for effective intervention and treatment. In recent years, computer vision techniques have emerged as robust tools for automated gait analysis, offering non-invasive, costeffective, and scalable solutions. However, existing approaches often overlook the critical aspect of privacy preservation. In this study, we propose the world’s pioneering computer vision-based abnormal gait detection system with a privacy-preserving mechanism. Specifically, we extract 2D skeletons from encrypted images using a deep neural network model, which is facilitated by an optical system incorporating a custom-made refractive optical element. These extracted features are then fed into machine learning models for the detection of normal versus abnormal gait patterns. Evaluations across various models including random forest, decision tree, K-nearest neighbor, support vector machine, neural network, and convolutional neural network reveal that the random forest model attains the highest classification performance based on 2D skeletons extracted from encrypted images.
Copyright (c) 2024 Afreen Naz, Pandey Shourya Prasad, Sheldon Mccall, Chan Chi Leung, Ifeoma Ochi, Liyun Gong, Miao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
The research paper aims to assess ML models for video-recorded gaits with an aim of classifying people into high or low risks to fall groups. Several ML algorithms were tried employing OpenPose for CV, with RF showing the best outcomes: 93% accuracy along with F1-score as well as balanced sensitivity (93.50%) as well as specificity (92.50%). Some important determining factors were speed per unit distance, angle among other statistical measures. In comparison to wearables-based DL approaches plus traditional fall detection methods, this study’s approach showed higher accuracy and adaptability within health care settings.
Copyright (c) 2024 Shina Samuel Kolawole, Gautam Siddharth Kashyap, Olamide Emmanuel Kolawole, Miao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection. Our approach includes a combination of vision-language models that segment the CT scans, which are then fed to a deep neural architecture, named RACNet, for Covid-19 detection. In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases. At first, SAM produces multiple part-based segmentation masks for each slice in the CT scan; then CLIP selects only the masks that are associated with the regions of interest (ROIs), i.e., the right and left lungs; finally SAM is given these ROIs as prompts and generates the final segmentation mask for the lungs. Experiments are presented across two Covid-19 annotated databases which illustrate the improved performance obtained when our method has been used for segmentation of the CT scans.
Copyright (c) 2024 Dimitrios Kollias, Anastasios Arsenos, James Wingate, Stefanos Kollias https://creativecommons.org/licenses/by-nc-sa/4.0
Use of personal mobile technologies for peer-based assessment of stress: a systematic literature review
The use of personal mobile technologies has grown in recent years, providing a method for collecting high-frequency and high-quality data on human behaviors and states, amongst the others, on stress levels. Mobile technologies can play a significant role in peer-based stress assessment, particularly in e-mental health and well-being. It is accessible, convenient, and reliable compared to traditional self-report methods, making it a popular choice for collecting data. This systematic literature review aimed to explore the use of mobile technologies for peer-based assessment of stress. We analyzed existing literature to understand how mobile technologies have been used to assess stress levels through peer feedback—from relatives, friends, or others with close and daily contact with the individual. The results of the review showed that mobile technologies have the potential to be a valuable tool for peer-based stress assessment, as they can provide real-time and convenient data collection. However, although its popularity has grown in recent years, it is worth noting that the use of paper and pen questionnaires has remained prevalent in peer-based stress assessment over the last decade. This indicates that there is still a need for further exploration and evaluation of the benefits and limitations of both methods.
Copyright (c) 2024 Antoine Bellanger, Igor Matias, Katarzyna Wac https://creativecommons.org/licenses/by-nc-sa/4.0
Anomaly Detection in Skull scanning Images based on Multi-sensor Fusion
INTRODUCTION: Skull bones typically possess complex structures and features. When scanned with ordinary sensors, they are easily affected by noise due to the small difference between abnormal areas and normal tissue. OBJECTIVES: In order to solve the problem of small differences between abnormal areas and normal tissues, which make them susceptible to noise interference, this paper proposes a multi-sensor fusion based skull scan image anomaly detection method. METHODS: Firstly, the frequency correction factor is utilized to modify the frequency domain characteristics of the sensor signal during the skull scanning image acquisition process, aiming to enhance signal quality and reduce noise impact during acquisition. Secondly, bilateral filters and discrete wavelet transform are employed to subject the skull scanning image to dual domain decomposition in spatial and transformation domains, aiding in distinguishing between normal and abnormal regions. Subsequently, the low-frequency fusion algorithm guided by filtering and the high-frequency fusion algorithm based on multi-scale morphological gradients are fused, and the fused dual frequency components are merged back into the original spatial domain to retain important details. The fused reconstructed image aids in improving the accuracy of anomaly detection. Finally, a backbone network with an auto encoder structure is established to learn the feature representation of fused images, and an unsupervised deep neural network is employed to establish a detection model for anomaly detection in skull scanning images. RESULTS: Through experiments, it has been demonstrated that the F1 score approaches 1, the ROC curve closely approaches the upper left corner, and the AUC value approaches 1 after applying the proposed method for anomaly detection in skull scanning images. CONCLUSION: This algorithm has high sensitivity and low specificity, achieving high detection accuracy and demonstrating good performance.
Copyright (c) 2024 Xiaochun Guo, Hashim Ali https://creativecommons.org/licenses/by-nc-sa/4.0
Sport injury imaging for deep blood flow distribution with laser speckle
When laser speckle program technology is used to measure the blood flow distribution of deep tissues (such as subcutaneous tissue) in sports injuries, the deep blood flow characteristics of sports injuries contain a large amount of turbid tissue fluid. Laser passing through turbid tissue fluid will produce strong interference static speckle, masking the dynamic speckle of blood flow distribution, resulting in poor imaging effect of blood flow characteristics. Propose laser speckle imaging optimization technology and apply it to the measurement of deep tissue blood flow distribution in sports injuries. Based on the principle of laser speckle imaging technology, the problems in laser speckle imaging of deep blood flow distribution characteristics in sports injuries are analyzed. An exponential Laplace loss function is introduced to reduce the amplitude of changes in blood flow characteristics in intra class sports injuries and collect deep blood flow distribution characteristics in sports injuries; On the basis of calculating the laser speckle contrast ratio, the blood volume flow rate is determined, and the blood volume flow rate data is combined with the laser speckle contrast ratio to achieve imaging of deep blood flow distribution in sports injuries. The experimental results show that the improved laser speckle imaging technology has better imaging effects in imaging the deep blood flow distribution of sports injuries; Compared with the comparison method, the DICE coefficient, average accuracy MPA, and global imaging index have all improved, indicating that this method can effectively improve the imaging effect and is feasible.
Copyright (c) 2024 Fu Huang, Dezhi Geng, Sravan Kumar Reddy M. https://creativecommons.org/licenses/by-nc-sa/4.0
Evaluating Physicians’ Satisfaction with Using eHealth System for Managing Patients in Primary and Secondary Care
INTRODUCTION: eHealth systems are raising both patient satisfaction and medical care. The proper workflow regulations and data exchange between primary and secondary healthcare are crucial. OBJECTIVES: Investigation of the major determinants influencing the physicians’ satisfaction while using an eHealth information system. METHODS: A survey of primary and secondary healthcare medical professionals was conducted in R.N. Macedonia. The categorical variables from the data analysis were presented and a logistic regression was carried out. RESULTS: The multiple logistic regression model was statistically significant for sufficient evidence to reject the null hypothesis in which the overall satisfaction rating of the eHealth system usage for managing patients and other healthcare services will not be affected by the other variables in favour of the alternative Ha. CONCLUSION: Various factors between primary and secondary healthcare professionals regarding system’s usage satisfaction are presented and studied. Various issues were revealed between both parties that should serve the policymakers and medical authorities for further improvements.
Copyright (c) 2024 Viktor Denkovski, Goce Gavrilov, Irena Stojmenovska, Vladimir Radevski https://creativecommons.org/licenses/by-nc-sa/4.0
Battery signal control model for large-scale IoT medical monitors under multipath interference
INTRODUCTION: In large-scale IOT medical monitors, the accurate control of battery signals has been facing the problem of multipath interference. Multipath interference causes the receiver to receive multiple signals propagating through different paths and interfering with each other, which results in an imbalance in the battery signal control based on the time delay of the "transmit-receive" signals. OBJECTIVES: To solve the multipath interference problem of existing battery signal control, this paper designs a battery signal control model for a large-scale IoT medical monitor. METHODS: Firstly, this paper uses time synchronization to align the time between the receiver and the transmitter to synchronize the communication signals of the acquisition system; Next, a transverse time-domain filter is used for modulation filtering; Then, a judgment feedback equalization algorithm is introduced in combination with a full-feedback filter to suppress the inter-code interference and improve the signal quality; Finally, a fractional interval equalizer is designed to adjust the weight coefficients of the equalizer taps, and implement intelligent battery signal control in multi-hop communication under multipath interference based on fractional interval and bit error rate (BER) feedback modulation. RESULTS: Experimental results have shown that after using the method described in this paper to control the communication signal of the monitor battery, the output signal is relatively stable, and the BER reaches 1×10-4 when the signal-to-noise ratio is equal to 18dB. The BER is low, and the carrier-to-noise ratio of the output signal is 0.73~0.85. The carrier-to-noise ratio always remains above 0.73. CONCLUSION: The technologies effectively deal with complex network environment and channel condition variation, ensuring balanced system control, and the signal control effect is outstanding.
Copyright (c) 2024 Shuhua Yang, Shengnan Zhang, Ding Chen, Syed Atif Moqurrab https://creativecommons.org/licenses/by-nc-sa/4.0
Study on artificial intelligence recognition pre-processing algorithm for cervical cancer
INTRODUCTION: Cervical cancer is the most common malignant tumor in the female reproductive system, with the number of deaths due to cervical cancer in developing countries accounting for 80% of the global total. In China, the incidence rate of cervical cancer is increasing year by year. At present, the commonly used methods for cervical cancer screening include TCT, HPV testing, TCT+HPV combined testing, FRD, and VIA/VILI. Among them, although TCT+HPV combined testing has high sensitivity and specificity, it is costly and time-consuming. VIA/VILI screening is cost-effective, easy to operate, and suitable for promotion in economically underdeveloped areas.However, VIA/VILI screening relies on the subjective judgment of doctors, so its accuracy is relatively low in rural areas of China with a large population and a lack of well-trained doctors. To address this issue, computer-aided diagnosis (CAD) technology is needed to improve the accuracy and reliability of VIA/VILI screening. OBJECTIVES: The implementation of artificial intelligence (AI)-based Visual Inspection with Acetic acid (VIA) screening and computer-aided diagnosis has the potential to significantly reduce the cost of cervical cancer screenings and increase the coverage rate of cervical cancer screenings, thus reducing the incidence rate of the disease. To this end, we have developed an AI preprocessing algorithm aimed at improving the accuracy of AI in detecting cervical cancer. METHODS: Initially, the algorithm maps images to the YCrCb and Lab color spaces. Unlike traditional enhancement methods that mainly focus on the luminance channel, our method primarily enhances the Cr channel in the YCrCb color space and a channel in the Lab color space. This paper innovatively proposes the LT_CLAHE algorithm to enhance the Cr channel, obtaining an enhanced image with a bias towards blue-green colors, and uses the WLS algorithm to enhance the a channel, obtaining an enhanced image with a bias towards red colors. Subsequently, the enhanced images from both color spaces are fused to eliminate color distortion. RESULTS: Experimental results show that our method significantly enhances the texture of lesions and outperforms traditional methods across various objective indicators. When the enhanced images from this paper are used as input for neural networks, there is also a significant increase in the accuracy of neural network detection.
Contrast Limited Adaptive Histogram Equalization
Copyright (c) 2024 Bo Feng, Chao Xu, Zhengping Li, Jusheng Li, Chuanyi Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Related factors with NCD in developing countries: economic, diet and risk factors dimensions
INTRODUCTION: Noncommunicable diseases (NCD), such as cardiovascular, oncological, respiratory diseases and diabetes mellitus, remain the leading cause of mortality worldwide. These diseases are associated with factors such as lack of physical activity, poor diet, smoking and excessive alcohol consumption. The economic and social cost of NCD in developed countries is considerable. In addition to the effects on the quality of life and health of individuals, these diseases generate a significant financial burden on health systems and the economy in general. The main causes of mortality together with an analysis of mathematical models, can provide fundamental information to monitor trends in the health outcomes, recognize the pattern of diseases that affect mortality and disability, identify emerging health challenges, evaluate the effectiveness of interventions and aid in health decision-making. OBJECTIVES: To evaluate the relationship of a selected set of economic, dietary health risk factors of the economically active population in 13 developing countries for the year 2019 in NCD. Apply a dimension reduction method to detect cross-sectional variability in the selected countries, carry out a behavioral analysis of the underlying variables, identify patterns and generate indices for monitoring related factors of NCD. METHODS: A database was built for the 2019 period of 13 developing countries including 76 variables, considering economic, food and lifestyle indicators. The principal components method was used to create new dimensions to group relevant information from all the variables used and characterize the diseases in developing countries for 4 selected NCD: cardiovascular disease, chronic respiratory disease, neoplasia, and diabetes mellitus. NCD monitoring indices were created considering an index of diet, economic and factors that affect the mortality. Using the generated indices, a cluster model was applied to group countries with similar characteristics according to the information analysed for each index. RESULTS: Some relevant characteristics were identified in the countries analyzed, as well as interesting patterns among the factors related to NCD. The countries could be grouped considering their economic and nutritional behavior. It was observed that Latin American countries and Poland behave similarly, just as Asian countries show a similarity in eating behavior. The economic indicators of investment in health, as well as hours worked, behave in a similar way. It was identified that there are certain foods that have a similar behavior both in their consumption and in how they affect NCD. Thanks to the elaboration of the indices, it was observed that the countries of the Middle East and North Africa have a better food balance, but not the countries of Latin America. CONCLUSION: The application of a dimensionality reduction method and cluster analysis out of quantitative methods made it possible to characterize the behavior of a set of variables that impact NCD, as well as to synthesize this information into specific indices by category of analysis. Strategies focused on improving NCD indicators can have a greater impact by identifying similar behavior profiles among developing countries, in the same way, joint policies could be designed to address NCD through specific actions by dimension of analysis and extend these policies to countries with similar profiles.
Copyright (c) 2024 Sergio Arturo Dominguez Miranda, Roman Rodríguez Aguilar https://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of an Infectious Disease Vaccination Prediction System Based on the MF-Conv LSTM Model
Infectious diseases can seriously threaten people's life safety and have a serious impact on social stability. Therefore, it should improve society’s stability under infectious diseases and ensure the safety of people's lives. A personnel flow feature extraction model based on Multi-Feature Convolutional Long Short-Term Memory (MF-Conv LSTM) is designed based on the characteristics of human daily activity behavior. This can optimize the accuracy of transmission simulation prediction for infectious disease vaccination. When using multi-feature ensemble analysis to extract human daily activity features as input for infectious disease simulation and prediction models, the learner's prediction score for the recurrent infectious diseases reached 0.8705. When using multi-feature ensemble analysis, the predicted scores did not exceed 0.85. The designed infectious disease vaccine transmission prediction model can accurately simulate the infectious behavior of infectious diseases. This provides direction for developing strategies to disrupt the infectious diseases’ spread. This reduces the infectious diseases’ harm to people's personal safety and improves social stability during the spread of large-scale infectious diseases.
Copyright (c) 2024 Ya Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network
Surface electromyography signals have significant value in gesture recognition due to their ability to reflect muscle activity in real time. However, existing gesture recognition technologies have not fully utilized surface electromyography signals, resulting in unsatisfactory recognition results. To this end, firstly, a Butterworth filter was adopted to remove high-frequency noise from the signal. A combined method of moving translation threshold was introduced to extract effective signals. Then, a gesture recognition model based on multi-stream feature fusion network was constructed. Feature extraction and fusion were carried out through multiple parallel feature extraction paths, combined with convolutional neural networks and residual attention mechanisms. Compared to popular methods of the same type, this new recognition method had the highest recognition accuracy of 92.1% and the lowest recognition error of 5%. Its recognition time for a single-gesture image was as short as 4s, with a maximum Kappa coefficient of 0.92. Therefore, this method combining multi-stream feature fusion networks can effectively improve the recognition accuracy and robustness of gestures and has high practical value.
Copyright (c) 2024 Xiaoyun Wang https://creativecommons.org/licenses/by-nc-sa/4.0
NLP and Machine Learning for Sentiment Analysis in COVID-19 Tweets: A Comparative Study
In response to the COVID-19 pandemic, a novel technique is given for assessing the sentiment of individuals using Twitter data obtained from the UCI repository. Our approach involves the identification of tweets with a discernible sentiment, followed by the application of specific data preprocessing techniques to enhance data quality. We have developed a robust model capable of effectively discerning the sentiments behind these tweets. To evaluate the performance of our model, we employ four distinct machine learning algorithms: logistic regres sion, decision tree, k-nearest neighbor and BLSTM. We classify the tweets into three categories: positive, neutral, and negative sentiments. Our performance evaluation is based on several key metrics, including accuracy, precision, recall, and F1-score. Our experimental results indicate that our proposed model excels in accurately capturing the perceptions of individuals regarding the COVID-19 pandemic.
Bidi- rectional Long Short-Term Memory (BLSTM)
Copyright (c) 2024 Shahedhadeennisa Shaik, Chaitra S P https://creativecommons.org/licenses/by-nc-sa/4.0
Tuberculosis detection bars on VGG19 transfer learning and Zebra Optimization Algorithm
Tuberculosis (TB) remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. This study introduces a novel approach combining VGG19, a deep convolutional neural network model, with a newly developed Zebra Optimization Algorithm (ZOA) to enhance the accuracy of TB detection from chest X-ray images. The Zebra Optimization Algorithm, inspired by the social behavior of zebras, was applied to optimize the hyperparameters of the VGG19 model, aiming to improve the model's generalizability and detection performance. Our method was evaluated using a well-defined metric system that included accuracy, sensitivity, and specificity. Results indicate that the combination of VGG19 and ZOA significantly outperforms traditional methods, achieving a high accuracy rate, which underscores the potential of hybrid approaches in TB image analysis.
Copyright (c) 2024 Tianzhi Le, Fanfeng Shi, Meng Ge, Ran Dong, Dan Shan https://creativecommons.org/licenses/by-nc-sa/4.0
A Novel Approach to Heart Disease Prediction Using Artificial Intelligence Techniques
INTRODUCTION: Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient prediction models OBJECTIVES: To research new models for heart disease prediction METHODS: This paper presents a novel approach for predicting heart disease using advanced artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms RESULTS By leveraging patient data and integrating various AI models, this approach aims to enhance prediction accuracy and support early diagnosis and intervention CONCLUSION: This study presents a novel AI-based approach for heart disease prediction, demonstrating the efficacy of ML and DL models in improving diagnostic accuracy
Copyright (c) 2024 V. Sathyavathy https://creativecommons.org/licenses/by-nc-sa/4.0
An Effective Lung Cancer Diagnosis Model Using the CNN Algorithm
The disease known as lung cancer is a serious condition that may be deadly if it is not diagnosed at an early stage. The diagnosis of lung cancer has to be improved, and there is a need for a cost-effective and user-friendly system that leverages state-of-the-art data science technology. This would help simplify operations, save time and money, and improve diagnosis. This research suggests the use of a convolutional neural network (CNN) architecture for the purpose of categorizing three unique histopathological pictures, namely benign, adenocarcinoma, and squamous cell carcinoma. The purpose of this study is to apply the CNN model to properly classify these three kinds of cancers and to compare the accuracy of the CNN model to the accuracy of other techniques that have been employed in investigations that are comparable to this one. The CNN model was not used in any of the preceding research for the purpose of categorizing these particular histopathological pictures; hence, the relevance of this work cannot be overstated. It is possible to get more positive treatment results by correctly classifying malignant tumors as early as possible. In training, the CNN model obtained an accuracy of 96.11%, and in validation, it earned an accuracy of 97.2%. The suggested method has the potential to improve lung cancer diagnosis in patients by classifying them into subgroups according to the symptoms they exhibit. This approach to machine learning, which makes use of the random forest technique, has the potential to reduce the amount of time, resources, and labor required. Utilizing the CNN model to categorize histopathological pictures may, ultimately, improve the diagnostic accuracy of lung cancer and save lives by allowing early disease identification.
Copyright (c) 2024 Sonia Kukreja, Munish Sabharwal https://creativecommons.org/licenses/by-nc-sa/4.0
A New Deepfake Detection Method Based on Compound Scaling Dual-Stream Attention Network
INTRODUCTION: Deepfake technology allows for the overlaying of existing images or videos onto target images or videos. The misuse of this technology has led to increasing complexity in information dissemination on the internet, causing harm to personal and societal public interests. OBJECTIVES: To reduce the impact and harm of deepfake as much as possible, an efficient deepfake detection method is needed. METHODS: This paper proposes a deepfake detection method based on a compound scaling dual-stream attention network, which combines a compound scaling module and a dual-stream attention module based on Swin Transformer to detect deepfake videos. In architectural design, we utilize the compound scaling module to extract shallowlevel features from the images and feed them into the deep-level feature extraction layer based on the dual-stream attention module. Finally, the obtained features are passed through a fully connected layer for classification, resulting in the detection outcome. RESULTS: Experiments on the FF++ dataset demonstrate that the deepfake detection accuracy is 95.62%, which shows its superiority to some extent. CONCLUSION: The method proposed in this paper is feasible and can be used to detect deepfake videos or images.
Copyright (c) 2024 Shuya Wang, Chenjun Du, Yunfang Chen https://creativecommons.org/licenses/by-nc-sa/4.0
An Innovative CBR-Enhanced Approach for Skin Cancer Classification using Cascade Forest Model and Convolutional Neural Network with Attention Mechanism
INTRODUCTION: In recent years, skin cancer has emerged as a pressing concern, necessitating advanced diagnostic and classification techniques. OBJECTIVES: This paper introduces an innovative hybrid approach that combines deep learning and machine learning to enhance the retrieval phase of the Case-Based Reasoning (CBR) system for skin cancer classification. METHODS: The proposed approach leverages a Convolutional Neural Network (CNN) with an attention mechanism for feature extraction, which is used to build the case base. Additionally, it uses a modified cascade forest model, augmented with traditional machine learning classifiers for classification. This modified cascade forest model incorporates the XGBoost model in its initial layer to facilitate more effective ensemble learning and bolster predictive performance. Subsequently, in the following layers, it use the random forest model to capitalize on its ability to handle high-dimensional feature spaces and maintain diversity within the ensemble. RESULTS: Rigorous experimentation on the balanced HAM10000 dermoscopic image dataset, employing the Synthetic Minority Oversampling Technique (SMOTE), demonstrates the superiority of the modified cascade forest model in multi-class skin cancer classification. This model consistently achieves the highest metrics, including accuracy (95.40%), precision (95.49%), F1-Score (95.38%), and recall (95.44%). CONCLUSION: This research highlights the efficacy of the proposed model compared to other classifiers, emphasizing the significance of the modified cascade forest model in enhancing the accuracy and reliability of skin cancer classification.
Copyright (c) 2024 Safa Gasmi, Akila DJEBBAR, Hayet Farida Merouani, Hanene Djedi https://creativecommons.org/licenses/by-nc-sa/4.0
Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms
INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19- related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform indepth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms.
Term Frequency – Inverse Document Frequency
Copyright (c) 2024 J Naeem, Omer Melih Gul, I B Parlak, K Karpouzis, Y B Salman, S N Kadry https://creativecommons.org/licenses/by-nc-sa/4.0
Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model
Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.
Copyright (c) 2024 S. Naganandhini, P. Shanmugavadivu https://creativecommons.org/licenses/by-nc-sa/4.0
Data Analysis and Predictive Modelling on Heart Disease based on People’s Lifestyle
Coronary Artery Disease (CAD) is a form of heart disease primarily influenced by lifestyle choices. Despite preventative measures available to mitigate CAD risks, a significant proportion of the population remains unaware of its severity and consequently neglects necessary precautions. As a result, the influence of CAD continues to rise. This project aims to curb CAD cases by developing an early warning detection and educational accessible to the general population, leveraging Machine Learning and Data Visualization technologies. Research indicates that while Coronary Artery Disease can be mitigated through a lifestyle shift towards healthier living, the risk remains due to factors such as age and natural health deterioration.
Copyright (c) 2024 Edward Leonardo, Murugananthan Velayutham, Justin Gilbert https://creativecommons.org/licenses/by-nc-sa/4.0
Automatic Data-Driven Classification Systems for Cardiovascular Disease
Cardiovascular disease (CVD) continues to contribute significantly to preventable deaths and avoidable disability worldwide. Prediction and prevention are of utmost importance in the support of public health. Machine learning and deep learning algorithms have emerged as powerful tools to improve the accuracy of diagnosis, prognosis, and treatment of cardiovascular disease. By employing these technologies, medical professionals can gain valuable insights into the risk factors associated with CVD. The focus of this research is to classify and predict cardiovascular diseases using techniques such as support vector machines, ensemble methods, decision trees, random forests, and neural networks. The effectiveness of these algorithms is evaluated based on metrics including accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results show that support vector machines and ensemble methods offer superior accuracy, while neural networks exhibit higher sensitivity and specificity in predicting cardiovascular diseases.
Copyright (c) 2024 Muralidharan Jayaraman, Shanmugavadivu Pichai https://creativecommons.org/licenses/by-nc-sa/4.0
Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction
Cancer, a malignant disease, results from abnormalities in the body cells that lead to uncontrolled growth and division, surpassing healthy growth and stability. In the case of breast cancer, this uncontrolled growth and division occurs in breast cells. Early identification of breast cancer is key to lowering mortality rates. Several new developments in artificial intelligence predictive models show promise for assisting decision-making. The primary goal of the proposed study is to build an efficient Breast Cancer Intelligent System using a multimodal dataset. The aim is to to establish Computer-Aided Diagnosis for breast cancer by integrating various data. This study uses the TCGA "The Cancer Genome Atlas Breast Invasive Carcinoma Collection" (TCGA-BRCA) dataset, which is part of an ongoing effort to create a community integrating cancer phenotypic and genotypic data. The TCGA- BRCA dataset includes: Clinical Data, RNASeq Gene Data, Mutation Data, and Methylation Data. Both clinical and genomic data are used in this study for breast cancer diagnosis. Integrating multiple data modalities enhances the robustness and precision of diagnostic and prognostic models in comparison with conventional techniques. The approach offers several advantages over unimodal models due to its ability to integrate diverse data sources. Additionally, these models can be employed to forecast the likelihood of a patient developing breast cancer in the near future, providing a valuable tool for early intervention and treatment planning.
Copyright (c) 2024 Shanmugavadivu Pichai, G. Kanimozhi, M. Mary Shanthi Rani, N.K. Riyaz https://creativecommons.org/licenses/by-nc-sa/4.0
Performance Assessment of Deep Learning Models on Non-Small Cell Lung Cancer Type Classification
In recent years, lung cancer incidents are very high with equally high mortality rate. The main reason for fatal incidences is the late diagnosis and confirmation of the disease at an advanced stage. Identification of the disease at an early stage using lung Computed Tomography (CT) offers tremendous scope for timely medical intervention. The article illustrates the use of deep transfer learning-based pre-trained models for the diagnosis of Non-Small Cell Lung Cancer (NSCLC). The datasets were chosen from Chest CT Scan Images and the Lung Image Database Consortium (LIDC), containing over 3,179 images depicting three NSCLC types, namely, normal, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. The process is designed to measure the accuracy of NSCLC detection with an experimental dataset using approaches with and without pre-processing of lung images. The transfer learning models use deep learning and produce good results in prediction and classification. The image dataset was first handled by the convolutional neural networks DenseNet121, ResNet50, InceptionV3, VGG16, Xception, and VGG19. As a second phase, input images were subjected to contrast/brightness enhancement using Multi Level Dualistic Sub Image Histogram Equalization (ML-DSIHE). Enhanced images were further processed using shape-based feature extraction. Finally, those features input to CNN models and the results recorded. Among these models, VGG16 achieved the highest accuracy of 81.42% using the original dataset and 91.64% with the enhanced dataset. The performance of these two approaches was also evaluated using Precision, Recall, F1-Score, Accuracy, and Loss. It is confirmed that VGG16 gives highly reliable accuracy when trained upon enhanced images.
Copyright (c) 2024 K. Ezhilraja, P. Shanmugavadivu https://creativecommons.org/licenses/by-nc-sa/4.0