Hadeel Alsolai, F. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar
Anomaly detection in pedestrian walkways of visually impaired people (VIP) is a vital research area that utilizes remote sensing and aids to optimize pedestrian traffic and improve flow. Researchers and engineers can formulate effective tools and methods with the power of machine learning (ML) and computer vision (CV) to identifying anomalies (i.e. vehicles) and mitigate potential safety hazards in pedestrian walkways. With recent advancements in ML and deep learning (DL) areas, authors have found that the image recognition problem ought to be devised as a two-class classification problem. Therefore, this manuscript presents a new sine cosine algorithm with deep learning-based anomaly detection in pedestrian walkways (SCADL-ADPW) algorithm. The proposed SCADL-ADPW technique identifies the presence of anomalies in the pedestrian walkways on remote sensing images. The SCADL-ADPW techniques focus on the identification and classification of anomalies, i.e. vehicles in the pedestrian walkways of VIP. To accomplish this, the SCADL-ADPW technique uses the VGG-16 model for feature vector generation. In addition, the SCA approach is designed for the optimal hyperparameter tuning process. For anomaly detection, the long short-term memory (LSTM) method can be exploited. The experimental results of the SCADL-ADPW technique are studied on the UCSD anomaly detection dataset. The comparative outcomes stated the improved anomaly detection results of the SCADL-ADPW technique.
{"title":"Assisting Visually Impaired People Using Deep Learning-based Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems on Remote Sensing Images","authors":"Hadeel Alsolai, F. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar","doi":"10.57197/jdr-2023-0021","DOIUrl":"https://doi.org/10.57197/jdr-2023-0021","url":null,"abstract":"Anomaly detection in pedestrian walkways of visually impaired people (VIP) is a vital research area that utilizes remote sensing and aids to optimize pedestrian traffic and improve flow. Researchers and engineers can formulate effective tools and methods with the power of machine learning (ML) and computer vision (CV) to identifying anomalies (i.e. vehicles) and mitigate potential safety hazards in pedestrian walkways. With recent advancements in ML and deep learning (DL) areas, authors have found that the image recognition problem ought to be devised as a two-class classification problem. Therefore, this manuscript presents a new sine cosine algorithm with deep learning-based anomaly detection in pedestrian walkways (SCADL-ADPW) algorithm. The proposed SCADL-ADPW technique identifies the presence of anomalies in the pedestrian walkways on remote sensing images. The SCADL-ADPW techniques focus on the identification and classification of anomalies, i.e. vehicles in the pedestrian walkways of VIP. To accomplish this, the SCADL-ADPW technique uses the VGG-16 model for feature vector generation. In addition, the SCA approach is designed for the optimal hyperparameter tuning process. For anomaly detection, the long short-term memory (LSTM) method can be exploited. The experimental results of the SCADL-ADPW technique are studied on the UCSD anomaly detection dataset. The comparative outcomes stated the improved anomaly detection results of the SCADL-ADPW technique.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88498006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Taking Power, Telling Stories: Using Collaborative Autoethnography to Explore Transitions to Adulthood with and without Disability Identities","authors":"Lauren Hislop, K. Davies, Shaylie Pryer","doi":"10.16993/sjdr.915","DOIUrl":"https://doi.org/10.16993/sjdr.915","url":null,"abstract":"","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67470311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ayouni, Mohamed Maddeh, Shaha T. Al-Otaibi, M. Alazzam, Nazik Alturki, Fahima Hajjej
An Internet of Things-based automated patient condition monitoring and detection system is discussed and built in this work. The proposed algorithm that underpins the smart-bed system is based on deep learning. The movement and posture of the patient’s body may be determined with the help of wearable sensor-based devices. In this work, an internet protocol camera device is used for monitoring the smart bed, and sensor data from five key points of the smart bed are core components of our approach. The Mask Region Convolutional Neural Network approach is used to extract data from many important areas from the body of the patient by collecting data from sensors. The distance and the time threshold are used to identify motions as being either connected with normal circumstances or uncomfortable ones. The information from these key locations is also utilised to establish the postures in which the patient is lying in while they are being treated on the bed. The patient’s body motion and bodily expression are constantly monitored for any discomfort if present. The results of the experiments demonstrate that the suggested system is valuable since it achieves a true-positive rate of 95% while only yielding a false-positive rate of 4%.
{"title":"Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions","authors":"S. Ayouni, Mohamed Maddeh, Shaha T. Al-Otaibi, M. Alazzam, Nazik Alturki, Fahima Hajjej","doi":"10.57197/jdr-2023-0017","DOIUrl":"https://doi.org/10.57197/jdr-2023-0017","url":null,"abstract":"An Internet of Things-based automated patient condition monitoring and detection system is discussed and built in this work. The proposed algorithm that underpins the smart-bed system is based on deep learning. The movement and posture of the patient’s body may be determined with the help of wearable sensor-based devices. In this work, an internet protocol camera device is used for monitoring the smart bed, and sensor data from five key points of the smart bed are core components of our approach. The Mask Region Convolutional Neural Network approach is used to extract data from many important areas from the body of the patient by collecting data from sensors. The distance and the time threshold are used to identify motions as being either connected with normal circumstances or uncomfortable ones. The information from these key locations is also utilised to establish the postures in which the patient is lying in while they are being treated on the bed. The patient’s body motion and bodily expression are constantly monitored for any discomfort if present. The results of the experiments demonstrate that the suggested system is valuable since it achieves a true-positive rate of 95% while only yielding a false-positive rate of 4%.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80249592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rheumatoid arthritis (RA), a chronic autoimmune disorder, can cause joint deformity and disability. The Janus kinases (JAKs), intracellular tyrosine kinases family (includes JAK1, JAK2, and JAK3), play an essential role in the signaling of various cytokines and are implicated in the pathogenesis of inflammatory diseases, including RA. Consequently, JAKs have attracted significant attention in recent years as therapeutic targets of RA. In the current study, we explored the role of a set of biomolecules from marine sources that could be used as specific inhibitors of JAKs and treat arthritis. The binding affinity of these molecules including astaxanthin (ATX), fucoxanthin (FX), fuscoside E (FsE), fucosterol (Fs), and phlorofucofuroeckol (PFFE) JAK3 has been analyzed. In addition, the details of relative structural interactions have been compared to those of the recently Food and Drug Administration-approved inhibitor, tofacitinib. Interestingly, some of these marine biomolecules showed a higher binding energy (b.e.) and specific binding to JAK3 active/potential sites when compared to the approved inhibitors. For instance, FsE binds to two key regulator residues of JAK3 required for its activity and for inhibitor stability, CYS909 and LYS905, with higher b.e. (-9.6) than the approved inhibitors. Thus, FsE may have a potential inhibitory action on JAKs and especially on JAK3. Additionally, PFFE can bind to several kinase critical regulators of JAK3 and the b.e. may reach -10.7. Based on the evaluation of oral availability, drug-likeness, pharmacokinetics, and medicinal chemistry friendliness, FsE seems to be the most appropriate potential inhibitor for JAK3.
类风湿性关节炎(RA)是一种慢性自身免疫性疾病，可导致关节畸形和残疾。Janus激酶(JAKs)，细胞内酪氨酸激酶家族(包括JAK1, JAK2和JAK3)，在各种细胞因子的信号传导中起重要作用，并与炎性疾病(包括RA)的发病机制有关。因此，jak作为类风湿性关节炎的治疗靶点近年来引起了人们的极大关注。在目前的研究中，我们探索了一组来自海洋的生物分子的作用，这些生物分子可以用作jak的特异性抑制剂并治疗关节炎。这些分子包括虾青素(ATX)、岩藻黄素(FX)、fuscoside E (FsE)、focus甾醇(Fs)和间苯二氟呋喃酚(PFFE) JAK3，它们的结合亲和力已被分析。此外，还将相关结构相互作用的细节与最近获得美国食品和药物管理局批准的抑制剂tofacitinib进行了比较。有趣的是，与已批准的抑制剂相比，其中一些海洋生物分子显示出更高的结合能(b.e)和对JAK3活性/潜在位点的特异性结合。例如，FsE结合JAK3的活性和抑制剂稳定性所需的两个关键调节残基，CYS909和LYS905，比批准的抑制剂具有更高的b.e值(-9.6)。因此，FsE可能对jakk，尤其是JAK3具有潜在的抑制作用。此外，PFFE可以结合JAK3的几个激酶关键调节因子，其b.e.可能达到-10.7。基于口服利用度、药物相似性、药代动力学和药物化学友好性的评估，FsE似乎是最合适的JAK3潜在抑制剂。
{"title":"Virtual Screening-based Molecular Analysis of Marine Bioactive Molecules as Inhibitors for Janus Kinase 3","authors":"E. Ahmed, S. Abdelsalam","doi":"10.57197/jdr-2023-0012","DOIUrl":"https://doi.org/10.57197/jdr-2023-0012","url":null,"abstract":"Rheumatoid arthritis (RA), a chronic autoimmune disorder, can cause joint deformity and disability. The Janus kinases (JAKs), intracellular tyrosine kinases family (includes JAK1, JAK2, and JAK3), play an essential role in the signaling of various cytokines and are implicated in the pathogenesis of inflammatory diseases, including RA. Consequently, JAKs have attracted significant attention in recent years as therapeutic targets of RA. In the current study, we explored the role of a set of biomolecules from marine sources that could be used as specific inhibitors of JAKs and treat arthritis. The binding affinity of these molecules including astaxanthin (ATX), fucoxanthin (FX), fuscoside E (FsE), fucosterol (Fs), and phlorofucofuroeckol (PFFE) JAK3 has been analyzed. In addition, the details of relative structural interactions have been compared to those of the recently Food and Drug Administration-approved inhibitor, tofacitinib. Interestingly, some of these marine biomolecules showed a higher binding energy (b.e.) and specific binding to JAK3 active/potential sites when compared to the approved inhibitors. For instance, FsE binds to two key regulator residues of JAK3 required for its activity and for inhibitor stability, CYS909 and LYS905, with higher b.e. (-9.6) than the approved inhibitors. Thus, FsE may have a potential inhibitory action on JAKs and especially on JAK3. Additionally, PFFE can bind to several kinase critical regulators of JAK3 and the b.e. may reach -10.7. Based on the evaluation of oral availability, drug-likeness, pharmacokinetics, and medicinal chemistry friendliness, FsE seems to be the most appropriate potential inhibitor for JAK3.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"12 1-4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72530151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the current terminology debate, little is known about the terminology experiences of people with disabilities and their relatives. Therefore, their interviews and letters to editors about disability terminology experiences published in Dutch newspapers between 1950 and 2020 were examined using inductive qualitative analysis. Three themes were derived. Contributors (1) objected to the use of particular terms and explained why a change in disability terminology was required; (2) argued that a change in disability terminology was viable; and (3) opposed proposed terminological changes. Contributors stated that derogatory and outmoded terms did not accurately depict the abilities of people with disabilities, resulting in stigmatisation and exclusion. Few contributors addressed a cross-disability perspective, and there was no mention of disability policy in the terminology debate. Meaningful associations between disability terminology experiences and the visibility and onset of the disability could be established. The newspaper contributions reflected the growing self-awareness of people with disabilities and their relatives.
{"title":"Layers of Disability Terminology Experiences of People with Disabilities and their Relatives: An Analysis of Dutch Newspapers between 1950–2020","authors":"Aartjan Ter Haar, S. Hilberink, A. Schippers","doi":"10.16993/sjdr.1000","DOIUrl":"https://doi.org/10.16993/sjdr.1000","url":null,"abstract":"Despite the current terminology debate, little is known about the terminology experiences of people with disabilities and their relatives. Therefore, their interviews and letters to editors about disability terminology experiences published in Dutch newspapers between 1950 and 2020 were examined using inductive qualitative analysis. Three themes were derived. Contributors (1) objected to the use of particular terms and explained why a change in disability terminology was required; (2) argued that a change in disability terminology was viable; and (3) opposed proposed terminological changes. Contributors stated that derogatory and outmoded terms did not accurately depict the abilities of people with disabilities, resulting in stigmatisation and exclusion. Few contributors addressed a cross-disability perspective, and there was no mention of disability policy in the terminology debate. Meaningful associations between disability terminology experiences and the visibility and onset of the disability could be established. The newspaper contributions reflected the growing self-awareness of people with disabilities and their relatives.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67468383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hadeel Alsolai, F. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar
Deep learning technique has been efficiently used for assisting visually impaired people in different tasks and enhancing total accessibility. Designing a vision-based anomaly detection method on surveillance video specially developed for visually challenged people could considerably optimize awareness and safety. While it is a complex process, there is potential to construct a system by leveraging machine learning and computer vision algorithms. Anomaly detection in surveillance video is a tedious process because of the uncertain definition of abnormality. In the complicated surveillance scenario, the types of abnormal events might co-exist and are numerous, like long-term abnormal activities, motion and appearance anomaly of objects, etc. Conventional video anomaly detection techniques could not identify this kind of abnormal action. This study designs an Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection (ICSO-VBAD) on surveillance videos technique for visually challenged people. The purpose of the ICSO-VBAD technique is to identify and classify the occurrence of anomalies for assisting visually challenged people. To obtain this, the ICSO-VBAD technique utilizes the EfficientNet model to produce a collection of feature vectors. In the ICSO-VBAD technique, the ICSO algorithm was exploited for the hyperparameter tuning of the EfficientNet model. For the identification and classification of anomalies, the adaptive neuro fuzzy inference system model was utilized. The simulation outcome of the ICSO-VBAD system was tested on benchmark datasets and the results pointed out the improvements of the ICSO-VBAD technique compared to recent approaches with respect to different measures.
{"title":"Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection on Surveillance Videos for Visually Challenged People","authors":"Hadeel Alsolai, F. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar","doi":"10.57197/jdr-2023-0024","DOIUrl":"https://doi.org/10.57197/jdr-2023-0024","url":null,"abstract":"Deep learning technique has been efficiently used for assisting visually impaired people in different tasks and enhancing total accessibility. Designing a vision-based anomaly detection method on surveillance video specially developed for visually challenged people could considerably optimize awareness and safety. While it is a complex process, there is potential to construct a system by leveraging machine learning and computer vision algorithms. Anomaly detection in surveillance video is a tedious process because of the uncertain definition of abnormality. In the complicated surveillance scenario, the types of abnormal events might co-exist and are numerous, like long-term abnormal activities, motion and appearance anomaly of objects, etc. Conventional video anomaly detection techniques could not identify this kind of abnormal action. This study designs an Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection (ICSO-VBAD) on surveillance videos technique for visually challenged people. The purpose of the ICSO-VBAD technique is to identify and classify the occurrence of anomalies for assisting visually challenged people. To obtain this, the ICSO-VBAD technique utilizes the EfficientNet model to produce a collection of feature vectors. In the ICSO-VBAD technique, the ICSO algorithm was exploited for the hyperparameter tuning of the EfficientNet model. For the identification and classification of anomalies, the adaptive neuro fuzzy inference system model was utilized. The simulation outcome of the ICSO-VBAD system was tested on benchmark datasets and the results pointed out the improvements of the ICSO-VBAD technique compared to recent approaches with respect to different measures.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89269623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The usage of radiological investigations is increasing rapidly in Saudi Arabia. It has been estimated that 7.1% of the populace in the Kingdom of Saudi Arabia is disabled. Out of 32.94 million citizens, 1,445,723 (52.2% males and 47.8% females) millions are considered disabled. Disabled individuals are frequently undergoing medical imaging procedures, and there are not enough studies regarding the risk of radiation exposure to disabled patients from these machines. This study aims to quantify the frequency of medical procedures and estimate the collective dose for disabled individuals to predict the overall cancer risk from medical exposure. A total of 108 computed tomography (CT) procedures were carried out for disabled patients. The procedures include the brain, chest, abdomen, pelvis, and cervical spine. A 128-slice CT machine was used in this study Philips Ingenuity (Philips, Netherlands). The CT machine is subjected to regular quality control tests to ensure compliance with national recommendations. In this study, 108 [11 (10.2%) females and 97 (89.8%) males] CT procedures were carried out for disabled patients at the radiology department, King Khalid Hospital and Prince Sultan Center. The average and standard deviation radiation dose per CT procedure [DLP (mGy.cm)] for the brain, chest, abdomen, pelvis, and cervical spine were 1183.4 ± 187, 352.8 ± 88, 654 ± 73, 803 ± 800, and 527 ± 186, respectively. The estimated cancer risk is 1 cancer per 1000 to 10,000 CT procedures. Patient doses are comparable with those of previous studies carried out for normal patients. However, the protection of disabled patients from unnecessary radiation exposure is crucial to reduce the projected radiation risks and minimize the number of repeated CT scans and unproductive radiation exposure.
{"title":"Assessment of the Radiation Exposure and Cancer Risks of Disabled People Undergoing Different Computed Tomography Scans","authors":"A. Sulieman, M. Almuwannis","doi":"10.57197/jdr-2023-0014","DOIUrl":"https://doi.org/10.57197/jdr-2023-0014","url":null,"abstract":"The usage of radiological investigations is increasing rapidly in Saudi Arabia. It has been estimated that 7.1% of the populace in the Kingdom of Saudi Arabia is disabled. Out of 32.94 million citizens, 1,445,723 (52.2% males and 47.8% females) millions are considered disabled. Disabled individuals are frequently undergoing medical imaging procedures, and there are not enough studies regarding the risk of radiation exposure to disabled patients from these machines. This study aims to quantify the frequency of medical procedures and estimate the collective dose for disabled individuals to predict the overall cancer risk from medical exposure. A total of 108 computed tomography (CT) procedures were carried out for disabled patients. The procedures include the brain, chest, abdomen, pelvis, and cervical spine. A 128-slice CT machine was used in this study Philips Ingenuity (Philips, Netherlands). The CT machine is subjected to regular quality control tests to ensure compliance with national recommendations. In this study, 108 [11 (10.2%) females and 97 (89.8%) males] CT procedures were carried out for disabled patients at the radiology department, King Khalid Hospital and Prince Sultan Center. The average and standard deviation radiation dose per CT procedure [DLP (mGy.cm)] for the brain, chest, abdomen, pelvis, and cervical spine were 1183.4 ± 187, 352.8 ± 88, 654 ± 73, 803 ± 800, and 527 ± 186, respectively. The estimated cancer risk is 1 cancer per 1000 to 10,000 CT procedures. Patient doses are comparable with those of previous studies carried out for normal patients. However, the protection of disabled patients from unnecessary radiation exposure is crucial to reduce the projected radiation risks and minimize the number of repeated CT scans and unproductive radiation exposure.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90481963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Alabdulkreem, Radwa Marzouk, Mesfer Alduhayyem, M. Al-Hagery, Abdelwahed Motwakel, M. A. Hamza
Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.
{"title":"Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People","authors":"E. Alabdulkreem, Radwa Marzouk, Mesfer Alduhayyem, M. Al-Hagery, Abdelwahed Motwakel, M. A. Hamza","doi":"10.57197/jdr-2023-0020","DOIUrl":"https://doi.org/10.57197/jdr-2023-0020","url":null,"abstract":"Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82483099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hasan Alkahtani, Theyazn H. H. Aldhyani, M. Alzahrani
In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.
{"title":"Early Screening of Autism Spectrum Disorder Diagnoses of Children Using Artificial Intelligence","authors":"Hasan Alkahtani, Theyazn H. H. Aldhyani, M. Alzahrani","doi":"10.57197/jdr-2023-0004","DOIUrl":"https://doi.org/10.57197/jdr-2023-0004","url":null,"abstract":"In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90683478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inclusion Opportunities of Work 4.0? Employment Realities of People with Disabilities in Germany","authors":"Jan Jochmaring, Jana York","doi":"10.16993/sjdr.896","DOIUrl":"https://doi.org/10.16993/sjdr.896","url":null,"abstract":"","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67470409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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