In today's digital era, old systems and processes must be rethought, and new technologies must be implemented to keep businesses competitive and growing. High global competition provides its own demands for business people to continue to improve product innovation by utilising existing technology to face this global challenge. Data collection techniques on business development in the digital era are conducted through online data analysis, literature studies obtained from Google Scholar, online surveys and social media monitoring to collect information on digital businesses. The use of technologies such as big data and sentiment analysis can also help in understanding the changes that occur in the digital business ecosystem. Technology and the internet have opened up new opportunities for businesses to reach a wider market, improve operational efficiency, and accelerate business growth. High global competition puts its own demands on businesses to continuously improve product innovation by utilising existing technology to face these global challenges. Businesses that succeed in the digital era are those that can adapt quickly and remain responsive to changes in the market and technology. Businesses need to adapt to technological developments and utilise them to improve business quality and expand market reach.
{"title":"BUSINESS DEVELOPMENT IN THE DIGITAL AGE","authors":"Helisia Margahana","doi":"10.47679/ijasca.v3i2.55","DOIUrl":"https://doi.org/10.47679/ijasca.v3i2.55","url":null,"abstract":"In today's digital era, old systems and processes must be rethought, and new technologies must be implemented to keep businesses competitive and growing. High global competition provides its own demands for business people to continue to improve product innovation by utilising existing technology to face this global challenge. Data collection techniques on business development in the digital era are conducted through online data analysis, literature studies obtained from Google Scholar, online surveys and social media monitoring to collect information on digital businesses. The use of technologies such as big data and sentiment analysis can also help in understanding the changes that occur in the digital business ecosystem. Technology and the internet have opened up new opportunities for businesses to reach a wider market, improve operational efficiency, and accelerate business growth. High global competition puts its own demands on businesses to continuously improve product innovation by utilising existing technology to face these global challenges. Businesses that succeed in the digital era are those that can adapt quickly and remain responsive to changes in the market and technology. Businesses need to adapt to technological developments and utilise them to improve business quality and expand market reach.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"111 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616362","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}
Arabic handwriting recognition (AHR) poses major challenges for pattern recognition due to the cursive script and visual similarity of Arabic characters. While deep learning demonstrates promise, architectural enhancements may further improve performance. This study presents an offline AHR approach using a convolutional neural network (CNN) with bidirectional long short-term memory (BLSTM) and connectionist temporal classification (CTC). By enhancing temporal modeling and context representations without segmentation requirements, this BLSTM-CTC-CNN framework with an integrated Word Beam Search (WBS) decoder achieved 94.58% accuracy on the IFN/ENIT database. Results highlight improved efficiency over prior works. This demonstrates continued advancement in sophisticated deep learning techniques for accurate AHR through specialized modeling of Arabic script cursive properties and decoding constraints. This research represents an advancement in the continuous development of progressively intricate and precise systems for handwriting recognition.
{"title":"Convolutional Arabic handwriting recognition system based BLSTM-CTC using WBS decoder","authors":"M. Rabi","doi":"10.47679/ijasca.v3i2.52","DOIUrl":"https://doi.org/10.47679/ijasca.v3i2.52","url":null,"abstract":"Arabic handwriting recognition (AHR) poses major challenges for pattern recognition due to the cursive script and visual similarity of Arabic characters. While deep learning demonstrates promise, architectural enhancements may further improve performance. This study presents an offline AHR approach using a convolutional neural network (CNN) with bidirectional long short-term memory (BLSTM) and connectionist temporal classification (CTC). By enhancing temporal modeling and context representations without segmentation requirements, this BLSTM-CTC-CNN framework with an integrated Word Beam Search (WBS) decoder achieved 94.58% accuracy on the IFN/ENIT database. Results highlight improved efficiency over prior works. This demonstrates continued advancement in sophisticated deep learning techniques for accurate AHR through specialized modeling of Arabic script cursive properties and decoding constraints. This research represents an advancement in the continuous development of progressively intricate and precise systems for handwriting recognition.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139620163","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 rapid digital growth in Indonesia has prompted the government to leverage digital-based services to address the needs and concerns of its citizens. In this context, the LAPOR (People's Online Aspiration and Complaints Service) mobile application and website have been established as a means for Indonesians to voice complaints, aspirations, and requests for information to government agencies. However, user satisfaction with the LAPOR application has been suboptimal, as evidenced by user ratings and comments on platforms such as Playstore and AppStore. This research aims to identify the factors influencing user satisfaction with the LAPOR mobile application in Indonesia. The study focuses on variables including ease of use, security privacy, system quality, speed of platform response, and attitude toward use. The research methodology involves primary data collection through the distribution of questionnaires via Google Form to Indonesian users residing in specific regions. The validity and reliability of the research are ensured through rigorous testing of dependent, independent, and intervening variables. The findings of the research highlight the significant influence of security privacy on system quality, ease of use on user satisfaction, and system quality on ease of use. These results provide valuable insights for the government of Indonesia to enhance the effectiveness of the LAPOR system in addressing public complaints and aspirations. By addressing these factors, the government can improve user satisfaction and engagement with the LAPOR mobile application, ultimately leading to more effective public service delivery and citizen engagement. Keywords: Mobile Application, User Satisfaction, Technology Acceptance Model (TAM), Public Complaints, Indonesia Public Service Delivery
{"title":"ANALYSIS OF FACTORS INFLUENCING SATISFACTION WITH USING THE MOBILE LAPOR APPLICATION IN INDONESIA","authors":"Theresa Karyn Wijaya","doi":"10.47679/ijasca.v3i2.51","DOIUrl":"https://doi.org/10.47679/ijasca.v3i2.51","url":null,"abstract":"The rapid digital growth in Indonesia has prompted the government to leverage digital-based services to address the needs and concerns of its citizens. In this context, the LAPOR (People's Online Aspiration and Complaints Service) mobile application and website have been established as a means for Indonesians to voice complaints, aspirations, and requests for information to government agencies. However, user satisfaction with the LAPOR application has been suboptimal, as evidenced by user ratings and comments on platforms such as Playstore and AppStore. This research aims to identify the factors influencing user satisfaction with the LAPOR mobile application in Indonesia. The study focuses on variables including ease of use, security privacy, system quality, speed of platform response, and attitude toward use. The research methodology involves primary data collection through the distribution of questionnaires via Google Form to Indonesian users residing in specific regions. The validity and reliability of the research are ensured through rigorous testing of dependent, independent, and intervening variables. The findings of the research highlight the significant influence of security privacy on system quality, ease of use on user satisfaction, and system quality on ease of use. These results provide valuable insights for the government of Indonesia to enhance the effectiveness of the LAPOR system in addressing public complaints and aspirations. By addressing these factors, the government can improve user satisfaction and engagement with the LAPOR mobile application, ultimately leading to more effective public service delivery and citizen engagement. \u0000Keywords: \u0000Mobile Application, User Satisfaction, Technology Acceptance Model (TAM), Public Complaints, Indonesia Public Service Delivery","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"52 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442129","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}
This article introduces a novel algorithm crafted for encrypting color images. The algorithm leverages chaotic principles and harnesses fresh substitution tables derived from independent linear congruence generators. These generators are dynamically sized based on pseudo-random vectors used in this technology. The proposed method initiates with the original image, vectorization, employing selected chaotic maps. The primary goal revolves around implementing a genetic operator tailored for image encryption, who integrates an enhanced Vigenère technique, incorporating novel confusion and diffusion functions derived from the previously established substitution tables. To gauge the effectiveness of this approach, numerous color images of varying dimensions and formats underwent testing using our algorithm. The yielded outcomes are both promising and gratifying, furnishing heightened security against recognized attacks.
{"title":"Utilizing a pseudo-random Linear Congruential Generator (LCG) S-Box for encoding color images through genetic crossover","authors":"A. Jarjar","doi":"10.47679/ijasca.v3i2.41","DOIUrl":"https://doi.org/10.47679/ijasca.v3i2.41","url":null,"abstract":"This article introduces a novel algorithm crafted for encrypting color images. The algorithm leverages chaotic principles and harnesses fresh substitution tables derived from independent linear congruence generators. These generators are dynamically sized based on pseudo-random vectors used in this technology. The proposed method initiates with the original image, vectorization, employing selected chaotic maps. The primary goal revolves around implementing a genetic operator tailored for image encryption, who integrates an enhanced Vigenère technique, incorporating novel confusion and diffusion functions derived from the previously established substitution tables. To gauge the effectiveness of this approach, numerous color images of varying dimensions and formats underwent testing using our algorithm. The yielded outcomes are both promising and gratifying, furnishing heightened security against recognized attacks.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"4 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156565","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}
Mohamed Amine Meddaoui, M. Erritali, Françoise Sailhan
Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, facemask detection is a key innovative technology that allows the identificationof the number of individuals wearing face masks. In this regard, this paperprovides a comparative study of several machine learning and deep learningalgorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder,GAN, U-Net GAN) that support mask detection.
{"title":"A Comparative Study of Embedded Learning Models IoT-based for real time Mask Detection","authors":"Mohamed Amine Meddaoui, M. Erritali, Françoise Sailhan","doi":"10.47679/ijasca.v3i2.49","DOIUrl":"https://doi.org/10.47679/ijasca.v3i2.49","url":null,"abstract":"Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, facemask detection is a key innovative technology that allows the identificationof the number of individuals wearing face masks. In this regard, this paperprovides a comparative study of several machine learning and deep learningalgorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder,GAN, U-Net GAN) that support mask detection.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"19 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156260","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}
Client-server architecture is a software model through which resources and requests are serviced over a network. The client requests a resource over a network, and the server receives the request, processes it, and responds appropriately. With this model, multiple users can simultaneously access and use resources. This paper provides an overview of the architecture, outlining its characteristics, advantages, disadvantages, different implementations of the architecture as well as the current and future of this architecture.
{"title":"Client-server Architecture, a Review","authors":"Geofrey Nyabuto","doi":"10.47679/ijasca.v3i1.48","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.48","url":null,"abstract":"Client-server architecture is a software model through which resources and requests are serviced over a network. The client requests a resource over a network, and the server receives the request, processes it, and responds appropriately. With this model, multiple users can simultaneously access and use resources. This paper provides an overview of the architecture, outlining its characteristics, advantages, disadvantages, different implementations of the architecture as well as the current and future of this architecture.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"27 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180511","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 increasingly widespread use of micro-bacteria in research to improve soil characteris-tics in the world of construction and also provide a significant increase in soil carrying ca-pacity so that this kind of research makes a very good contribution where the material and mixing object are natural materials, namely in the form of bacteria. This study used the finite element method with the help of LISA V.8 FEA (Li-cense), a finite element method software package, to obtain the stress arising from existing soil ma-terial with soil material that has been mixed with mycobac-teria or the biogrouting method. According from the results of the analysis using numerical analysis using the finite el-ement method LISA V.8 FEA program, it can be seen that there is a reduc-tion in the oc-currence of settlement after adding the two bacteria which have been aged for 30 days with a reduction in soil settlement of 2.9-2.27 mm de-spite an increase in stress. ranging from 58.3 to 69.4 kN/m2.
微生物细菌在研究中的应用日益广泛,不仅可以改善建筑领域的土壤特性,还能显著提高土壤的承载能力,因此,当材料和混合对象都是天然材料(即细菌形式)时,这种研究会做出很好的贡献。本研究利用有限元法软件包 LISA V.8 FEA (Li-cense),采用有限元法获得了现有土壤材料与混合了真菌的土壤材料或生物布道法产生的应力。根据使用有限元分析法 LISA V.8 FEA 程序进行数值分析的结果,可以看出,在添加两种菌种 30 天后,沉降发生率有所降低,土壤沉降量减少了 2.9-2.27 毫米,尽管应力增加了 58.3 至 69.4 千牛/平方米。
{"title":"Characteristic behavior of soil using bacterial biogrouting with LISA FEA V.8.","authors":"A. W. Efendi","doi":"10.47679/ijasca.v3i1.47","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.47","url":null,"abstract":"The increasingly widespread use of micro-bacteria in research to improve soil characteris-tics in the world of construction and also provide a significant increase in soil carrying ca-pacity so that this kind of research makes a very good contribution where the material and mixing object are natural materials, namely in the form of bacteria. This study used the finite element method with the help of LISA V.8 FEA (Li-cense), a finite element method software package, to obtain the stress arising from existing soil ma-terial with soil material that has been mixed with mycobac-teria or the biogrouting method. According from the results of the analysis using numerical analysis using the finite el-ement method LISA V.8 FEA program, it can be seen that there is a reduc-tion in the oc-currence of settlement after adding the two bacteria which have been aged for 30 days with a reduction in soil settlement of 2.9-2.27 mm de-spite an increase in stress. ranging from 58.3 to 69.4 kN/m2.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"60 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263393","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}
Recent technological advances aim to recognise user data using biometrics such as the face, fingerprint, hand veins and iris. Currently, face prints are widely used to verify user data in e-passports. As a result, institutions face substantial difficulties in maintaining an appropriate level of security. Human error can introduce flaws that undermine security mechanisms. One potential solution to this problem is to install a facial recognition security system. Both hardware and software components make up this system, with the hardware being a camera and the software comprising face detection and identification algorithms. The purpose of this essay is to provide a thorough understanding of the Face Recognition Security System, including its application and deployment. Furthermore, the essay investigates the various weaknesses and methods of attack that could be used to target the system. The purpose of addressing these factors is to improve the effectiveness and robustness of system security, notably e-passport security.
{"title":"E-passport security systems and attack implications","authors":"Kaznah Alshammari","doi":"10.47679/ijasca.v3i1.38","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.38","url":null,"abstract":"Recent technological advances aim to recognise user data using biometrics such as the face, fingerprint, hand veins and iris. Currently, face prints are widely used to verify user data in e-passports. As a result, institutions face substantial difficulties in maintaining an appropriate level of security. Human error can introduce flaws that undermine security mechanisms. One potential solution to this problem is to install a facial recognition security system. Both hardware and software components make up this system, with the hardware being a camera and the software comprising face detection and identification algorithms. The purpose of this essay is to provide a thorough understanding of the Face Recognition Security System, including its application and deployment. Furthermore, the essay investigates the various weaknesses and methods of attack that could be used to target the system. The purpose of addressing these factors is to improve the effectiveness and robustness of system security, notably e-passport security.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"66 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139273184","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 COVID-19 pandemic has significantly impacted the United Arab Emirates (UAE), necessitating effective and accurate forecasting tools to inform public health policies and strategies. This study presents a comparative analysis of advanced deep-learning models for predicting COVID-19 cases in the UAE. We investigate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer perceptron, and Recurrent Neural Networks (RNN). The models are trained and evaluated using a comprehensive dataset of confirmed cases, demographic information, and relevant socio-economic indicators. The models are further optimized using a Bayesian optimizer and comparison is performed before and after the optimization of models. We have used predictive and perspective analytics on the COVID-19 dataset. Our research goal is to identify the most accurate and reliable model for forecasting COVID-19 cases in the region. The results demonstrate the effectiveness of these deep learning techniques in predicting COVID-19 cases, with each model exhibiting varying levels of accuracy and precision. A thorough and rigorous evaluation of the models' performances reveals the most suitable architecture for the UAE's specific context. This study contributes to the ongoing efforts to combat the pandemic by providing valuable insights into the application of advanced deep-learning models for accurate and timely COVID-19 case predictions. It was found that the RNN model performed the best without any optimization. The findings have significant implications for public health decision-making, enabling authorities to develop targeted and data-driven interventions to curb the spread of the virus and mitigate its impact on the UAE's population. This demonstrates the potential of deep learning algorithms in handling complex datasets and making accurate predictions which is a valuable capability to enhance accuracy in professional and healthcare environments.
{"title":"Unraveling COVID-19 Progression: A Comprehensive Com-parison of Advanced Deep Learning Methods for Precise Pre-dictions","authors":"Muhammad Usman Tariq, Shuhaida Binti Ismail","doi":"10.47679/ijasca.v3i1.37","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.37","url":null,"abstract":"The COVID-19 pandemic has significantly impacted the United Arab Emirates (UAE), necessitating effective and accurate forecasting tools to inform public health policies and strategies. This study presents a comparative analysis of advanced deep-learning models for predicting COVID-19 cases in the UAE. We investigate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer perceptron, and Recurrent Neural Networks (RNN). The models are trained and evaluated using a comprehensive dataset of confirmed cases, demographic information, and relevant socio-economic indicators. The models are further optimized using a Bayesian optimizer and comparison is performed before and after the optimization of models. We have used predictive and perspective analytics on the COVID-19 dataset. Our research goal is to identify the most accurate and reliable model for forecasting COVID-19 cases in the region. The results demonstrate the effectiveness of these deep learning techniques in predicting COVID-19 cases, with each model exhibiting varying levels of accuracy and precision. A thorough and rigorous evaluation of the models' performances reveals the most suitable architecture for the UAE's specific context. This study contributes to the ongoing efforts to combat the pandemic by providing valuable insights into the application of advanced deep-learning models for accurate and timely COVID-19 case predictions. It was found that the RNN model performed the best without any optimization. The findings have significant implications for public health decision-making, enabling authorities to develop targeted and data-driven interventions to curb the spread of the virus and mitigate its impact on the UAE's population. This demonstrates the potential of deep learning algorithms in handling complex datasets and making accurate predictions which is a valuable capability to enhance accuracy in professional and healthcare environments.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139278886","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}
This research is based on two areas related to Alzheimer's disease, the first is the early detection and diagnosis of Alzheimer's disease using deep learning techniques and its various algorithms, and the second relates to how to monitor and follow up on Alzheimer's disease using the Internet of Things (IOT). In this paper, a new diagnosis based on deep machine learning and monitoring of diseases similar to Alzheimer's is proposed. Diagnosis of Alzheimer's-like diseases is achieved through deep learning magnetic resonance imaging (MRI) analysis followed by an activity tracking framework to monitor people's activities in daily life using wearable inertial sensors. Activity monitoring provides a framework for assistance in activities of daily living and assessment of patient deterioration based on activity level. The results of Alzheimer's diagnosis show an improvement of up to 86.34% with respect to current known techniques. Furthermore, greater than 95% accuracy was achieved for classifying activities of daily living, which is very encouraging in looking at the subject's activity profile.
{"title":"IoT-based intelligent system For Alzheimer's Disease Detection & Monitoring","authors":"Mohamed Riad","doi":"10.47679/ijasca.v3i1.39","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.39","url":null,"abstract":"This research is based on two areas related to Alzheimer's disease, the first is the early detection and diagnosis of Alzheimer's disease using deep learning techniques and its various algorithms, and the second relates to how to monitor and follow up on Alzheimer's disease using the Internet of Things (IOT). In this paper, a new diagnosis based on deep machine learning and monitoring of diseases similar to Alzheimer's is proposed. Diagnosis of Alzheimer's-like diseases is achieved through deep learning magnetic resonance imaging (MRI) analysis followed by an activity tracking framework to monitor people's activities in daily life using wearable inertial sensors. Activity monitoring provides a framework for assistance in activities of daily living and assessment of patient deterioration based on activity level. The results of Alzheimer's diagnosis show an improvement of up to 86.34% with respect to current known techniques. Furthermore, greater than 95% accuracy was achieved for classifying activities of daily living, which is very encouraging in looking at the subject's activity profile.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"91 0","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279250","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}