The analysis and interpretation portion of the study concentrated on managing blockchain technology and on comprehending the rate of technological advancement in actions that support the development of the sustainability and healthcare ecosystems. The study reports results of a survey from 50 respondents who were directly handling activities in the healthcare industry. The study's discussion and findings section also included information on the analysis of the survey data that was acquired. This information is crucial for raising the healthcare ecosystem's to sustainability level. Blockchain technology is one of the most significant breakthroughs of recent times. By consistently changing the healthcare industry, it advances the entire field. It is seen as a series of building blocks that preserves interpersonal trust and covers vital information. The rapid development of blockchain technology is enabling a wide variety of uses across many industries. The potential of blockchain technology will be revolutionised by the systematic production of literature evaluations. One illustration of how swiftly the world has gone digital is the emergence of countless electronic records in the healthcare industry. Blockchain technology has made it possible to eliminate third-party administration's risks in the medical industry.
{"title":"Blockchain Technology in Enhancing Health Care Ecosystem for Sustainable Development","authors":"Sofiene Mansouri, Souhir Chabchoub, Yousef Alharbi, Abdulrahman Alqahtani","doi":"10.58346/jowua.2023.i3.018","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.018","url":null,"abstract":"The analysis and interpretation portion of the study concentrated on managing blockchain technology and on comprehending the rate of technological advancement in actions that support the development of the sustainability and healthcare ecosystems. The study reports results of a survey from 50 respondents who were directly handling activities in the healthcare industry. The study's discussion and findings section also included information on the analysis of the survey data that was acquired. This information is crucial for raising the healthcare ecosystem's to sustainability level. Blockchain technology is one of the most significant breakthroughs of recent times. By consistently changing the healthcare industry, it advances the entire field. It is seen as a series of building blocks that preserves interpersonal trust and covers vital information. The rapid development of blockchain technology is enabling a wide variety of uses across many industries. The potential of blockchain technology will be revolutionised by the systematic production of literature evaluations. One illustration of how swiftly the world has gone digital is the emergence of countless electronic records in the healthcare industry. Blockchain technology has made it possible to eliminate third-party administration's risks in the medical industry.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135081321","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}
Pub Date : 2023-09-30DOI: 10.58346/jowua.2023.i3.015
Rika Rosnelly, Bob Subhan Riza, Suparni S.
Malaria is a dangerous infectious disease because, if it is slow to handle, it can even cause death. Malaria is caused by a parasite called plasmodium, which is transmitted through the bite of a malaria mosquito called Anopheles. Parasites transmitted by mosquitoes attack human blood cells. The inspection method used to identify the type of malaria parasite is microscopic examination, whose accuracy and efficiency depend on human expertise. Examination methods using the Rapid Diagnostic Test (RDT) and Polymerase Chain Reaction (PCR) are not affordable, especially in underprivileged areas. This study compares the performance of classification methods, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN), to identify the type of malaria parasite and its stage and develop a feature extraction algorithm. The method of feature extraction is a decisive step to identifying the type of malaria parasite. The feature extraction process by developing a feature extraction algorithm is called the PEMA and KEHE feature tracking algorithm, or feature tracking with perimeter, eccentricity, metric, area, contrast, energy, homogeneity, and entropy. The classifier uses a convolutional neural network (CNN) to divide the samples into 16 classes. The experiment used 446 images of malaria parasites. The outcome of identification showed that by tracking the PEMA and KEHE features with the SVM classifier, the best accuracy value was 85.08%, compared to CNN with an accuracy value of 61.40%.
{"title":"Comparative Analysis of Support Vector Machine and Convolutional Neural Network for Malaria Parasite Classification and Feature Extraction","authors":"Rika Rosnelly, Bob Subhan Riza, Suparni S.","doi":"10.58346/jowua.2023.i3.015","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.015","url":null,"abstract":"Malaria is a dangerous infectious disease because, if it is slow to handle, it can even cause death. Malaria is caused by a parasite called plasmodium, which is transmitted through the bite of a malaria mosquito called Anopheles. Parasites transmitted by mosquitoes attack human blood cells. The inspection method used to identify the type of malaria parasite is microscopic examination, whose accuracy and efficiency depend on human expertise. Examination methods using the Rapid Diagnostic Test (RDT) and Polymerase Chain Reaction (PCR) are not affordable, especially in underprivileged areas. This study compares the performance of classification methods, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN), to identify the type of malaria parasite and its stage and develop a feature extraction algorithm. The method of feature extraction is a decisive step to identifying the type of malaria parasite. The feature extraction process by developing a feature extraction algorithm is called the PEMA and KEHE feature tracking algorithm, or feature tracking with perimeter, eccentricity, metric, area, contrast, energy, homogeneity, and entropy. The classifier uses a convolutional neural network (CNN) to divide the samples into 16 classes. The experiment used 446 images of malaria parasites. The outcome of identification showed that by tracking the PEMA and KEHE features with the SVM classifier, the best accuracy value was 85.08%, compared to CNN with an accuracy value of 61.40%.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039016","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}
Pub Date : 2023-09-30DOI: 10.58346/jowua.2023.i3.001
Rosa Clavijo-López, Jesús Merino Velásquez, Wayky Alfredo Luy Navarrete, Cesar Augusto Flores Tananta, Dorothy Luisa Meléndez Morote, Maria Aurora Gonzales Vigo, Doris Fuster- Guillén
Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.
{"title":"Energy-aware and Context-aware Fault Detection Framework for Wireless Sensor Networks","authors":"Rosa Clavijo-López, Jesús Merino Velásquez, Wayky Alfredo Luy Navarrete, Cesar Augusto Flores Tananta, Dorothy Luisa Meléndez Morote, Maria Aurora Gonzales Vigo, Doris Fuster- Guillén","doi":"10.58346/jowua.2023.i3.001","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.001","url":null,"abstract":"Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039628","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}
Pub Date : 2023-09-30DOI: 10.58346/jowua.2023.i3.006
Nikitina Vlada, Raúl A. Sánchez-Ancajima, Miguel Ángel Torres Rubio, Walter Antonio Campos- Ugaz, Anibal Mejía Benavides, María Del Rocío Hende-Santolaya, Jacqueline C. Ponce-Meza
Wireless technologies have grown in popularity and are used in many applications. Transient Mobile Ad hoc Networks (MANETs) serve specific goals without infrastructure. The dynamism of these networks makes them useful for ubiquitous computing. However, high mobility, the lack of a centralized authority, and open media make MANETs vulnerable to various security risks. Thus, an Intrusion Detection System (IDS) should be used to monitor and detect system security issues. To prevent and improve security against unauthorized access, intrusion screening is crucial. The depletion of a mobile node's power supply can impact its capacity to transmit packets, as this functionality is contingent upon the system's overall lifespan. Computational Optimization-driven solutions have been prevalent in the context of IDS and secure routing inside MANETs. This research employs the Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm (EPSO-IDSRA). Enhanced Particle Swarm Optimization technique (EPSO) has provided confidence-based, secure, and energy-efficient routing in MANETs. The EPSO technique is applied to identify optimal hops for enhancing the routing process. The initial step involves the activation of the fuzzy clustering algorithm, followed by selecting Cluster Heads (CHs) based on assessing their indirect, direct, and recent confidence values. Furthermore, the identification of value nodes was contingent upon assessing confidence levels. Also, the CHs are involved in multi-hop routing, and determining the optimal route depends on the anticipated protocol, which chooses the most favorable paths considering factors such as latency, throughput, and connectivity within the designated area. The EPSO method, presented for secure routing (at time 50ms), yielded an optimal energy consumption of 0.15 millijoules, a minimal delay of 0.008 milliseconds, a maximum throughput of 0.8 bits per second, and an 89% detection rate.
{"title":"Enhancing Security in Mobile Ad Hoc Networks: Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm","authors":"Nikitina Vlada, Raúl A. Sánchez-Ancajima, Miguel Ángel Torres Rubio, Walter Antonio Campos- Ugaz, Anibal Mejía Benavides, María Del Rocío Hende-Santolaya, Jacqueline C. Ponce-Meza","doi":"10.58346/jowua.2023.i3.006","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.006","url":null,"abstract":"Wireless technologies have grown in popularity and are used in many applications. Transient Mobile Ad hoc Networks (MANETs) serve specific goals without infrastructure. The dynamism of these networks makes them useful for ubiquitous computing. However, high mobility, the lack of a centralized authority, and open media make MANETs vulnerable to various security risks. Thus, an Intrusion Detection System (IDS) should be used to monitor and detect system security issues. To prevent and improve security against unauthorized access, intrusion screening is crucial. The depletion of a mobile node's power supply can impact its capacity to transmit packets, as this functionality is contingent upon the system's overall lifespan. Computational Optimization-driven solutions have been prevalent in the context of IDS and secure routing inside MANETs. This research employs the Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm (EPSO-IDSRA). Enhanced Particle Swarm Optimization technique (EPSO) has provided confidence-based, secure, and energy-efficient routing in MANETs. The EPSO technique is applied to identify optimal hops for enhancing the routing process. The initial step involves the activation of the fuzzy clustering algorithm, followed by selecting Cluster Heads (CHs) based on assessing their indirect, direct, and recent confidence values. Furthermore, the identification of value nodes was contingent upon assessing confidence levels. Also, the CHs are involved in multi-hop routing, and determining the optimal route depends on the anticipated protocol, which chooses the most favorable paths considering factors such as latency, throughput, and connectivity within the designated area. The EPSO method, presented for secure routing (at time 50ms), yielded an optimal energy consumption of 0.15 millijoules, a minimal delay of 0.008 milliseconds, a maximum throughput of 0.8 bits per second, and an 89% detection rate.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135038433","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}
Pub Date : 2023-09-30DOI: 10.58346/jowua.2023.i3.008
Dessy Adriani, Ratna Dewi, Leni Saleh, D. Yadi Heryadi, Fatma Sarie, I Gede Iwan Sudipa, Robbi Rahim
About 80% of the world's medicinal plants grow in Indonesia. The Negeri Rempah Foundation also said that Indonesia has more kinds of spices than any other country in Southeast Asia. To figure out the best way to export plants, you need to do a study that groups them by their main destination country. This is called "clustering," and it can be done by doing regional mapping. In the clustering process, measuring deviation/distance or distance space is a key part of figuring out how similar or regular data and items are. K-Medoids is one way to group things together. K-Medoids is an algorithm that groups data based on how far apart they are. Distance Measure is a way to measure the distance between two points. It can help an algorithm sort object into groups based on how similar their variables are. The dataset used comes from the Customs Documents of the Directorate General of Customs and Excise on the official website of the Central Bureau of Statistics for the period of 2012-2021 about the Export of Medicinal, Aromatic, and Spices Plants. This study uses mixed measures (mixed euclideandistance), numerical measures (camberradistance), and bregmandivergences (generalizeddivergence). The mapping results are compared with the validation of the Davies Bouldin Index (DBI). With the help of the rapidminer software, a number of tests were done. The results showed that using mixed measures (mixed euclideandistance) with a value of k=4 gave a DBI value of 0.021. Because it gives a DBI value close to 0, the K-Medoids algorithm with mixed measures (mixedeuclideandistance) is thought to work better than other distance measures.
{"title":"Using Distance Measure to Perform Optimal Mapping with the K-Medoids Method on Medicinal Plants, Aromatics, and Spices Export","authors":"Dessy Adriani, Ratna Dewi, Leni Saleh, D. Yadi Heryadi, Fatma Sarie, I Gede Iwan Sudipa, Robbi Rahim","doi":"10.58346/jowua.2023.i3.008","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.008","url":null,"abstract":"About 80% of the world's medicinal plants grow in Indonesia. The Negeri Rempah Foundation also said that Indonesia has more kinds of spices than any other country in Southeast Asia. To figure out the best way to export plants, you need to do a study that groups them by their main destination country. This is called \"clustering,\" and it can be done by doing regional mapping. In the clustering process, measuring deviation/distance or distance space is a key part of figuring out how similar or regular data and items are. K-Medoids is one way to group things together. K-Medoids is an algorithm that groups data based on how far apart they are. Distance Measure is a way to measure the distance between two points. It can help an algorithm sort object into groups based on how similar their variables are. The dataset used comes from the Customs Documents of the Directorate General of Customs and Excise on the official website of the Central Bureau of Statistics for the period of 2012-2021 about the Export of Medicinal, Aromatic, and Spices Plants. This study uses mixed measures (mixed euclideandistance), numerical measures (camberradistance), and bregmandivergences (generalizeddivergence). The mapping results are compared with the validation of the Davies Bouldin Index (DBI). With the help of the rapidminer software, a number of tests were done. The results showed that using mixed measures (mixed euclideandistance) with a value of k=4 gave a DBI value of 0.021. Because it gives a DBI value close to 0, the K-Medoids algorithm with mixed measures (mixedeuclideandistance) is thought to work better than other distance measures.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039765","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}
Pub Date : 2023-09-30DOI: 10.58346/jowua.2023.i3.017
Yus Hermansyah
Recent breakthroughs in deep learning have led to the development of cutting-edge industrial applications of Communicative Artificial Intelligence (AI), making it indispensable for businesses aiming to maintain a competitive edge. Consequently, artificial intelligence is no longer exclusive to large corporations; it now impacts businesses of all sizes, including small and medium-sized enterprises (SMEs), serving as a tool for command in the production and communication of crucial business aspects. This article delves into the extent of Communicative AI adoption by SMEs in Indonesia, shedding light on issues related to implementing industrial AI applications. To achieve this, a sample of SMEs participated in a structured online survey. Currently, AI adoption among SMEs in Indonesia is minimal. The reluctance is primarily attributed to high costs, extended duration, and inherent risks of developing proprietary applications. Instead, SMEs are heavily relying on AI-as-a-service and other cloud-based solutions. Various factors contribute to businesses' hesitancy. The slow progress in SME implementation indicates misunderstandings related to data and a lack of knowledge, influencing how these enterprises perceive the obstacles they encounter.
{"title":"Assessing the Impact of Communicative Artificial Intelligence Based Accounting Information Systems on Small and Medium Enterprises","authors":"Yus Hermansyah","doi":"10.58346/jowua.2023.i3.017","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.017","url":null,"abstract":"Recent breakthroughs in deep learning have led to the development of cutting-edge industrial applications of Communicative Artificial Intelligence (AI), making it indispensable for businesses aiming to maintain a competitive edge. Consequently, artificial intelligence is no longer exclusive to large corporations; it now impacts businesses of all sizes, including small and medium-sized enterprises (SMEs), serving as a tool for command in the production and communication of crucial business aspects. This article delves into the extent of Communicative AI adoption by SMEs in Indonesia, shedding light on issues related to implementing industrial AI applications. To achieve this, a sample of SMEs participated in a structured online survey. Currently, AI adoption among SMEs in Indonesia is minimal. The reluctance is primarily attributed to high costs, extended duration, and inherent risks of developing proprietary applications. Instead, SMEs are heavily relying on AI-as-a-service and other cloud-based solutions. Various factors contribute to businesses' hesitancy. The slow progress in SME implementation indicates misunderstandings related to data and a lack of knowledge, influencing how these enterprises perceive the obstacles they encounter.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135040237","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}
Pub Date : 2023-09-06DOI: 10.1007/s11036-023-02205-8
Li Gang, Huanbin Zhao, Tongzhou Zhao
{"title":"Document Vector Representation with Enhanced Features Based on Doc2VecC","authors":"Li Gang, Huanbin Zhao, Tongzhou Zhao","doi":"10.1007/s11036-023-02205-8","DOIUrl":"https://doi.org/10.1007/s11036-023-02205-8","url":null,"abstract":"","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87688123","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}
Pub Date : 2023-09-06DOI: 10.1007/s11036-023-02179-7
W.V. Siricharoen
{"title":"Improving User Experience (UX) by Applying (Interactive) Infographic in the Human Computer Interaction Context","authors":"W.V. Siricharoen","doi":"10.1007/s11036-023-02179-7","DOIUrl":"https://doi.org/10.1007/s11036-023-02179-7","url":null,"abstract":"","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82252973","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}
Pub Date : 2023-09-05DOI: 10.1007/s11036-023-02136-4
D. Leith
{"title":"Contact Tracing App Privacy: What Data is Shared by Non-GAEN Contact Tracing Apps","authors":"D. Leith","doi":"10.1007/s11036-023-02136-4","DOIUrl":"https://doi.org/10.1007/s11036-023-02136-4","url":null,"abstract":"","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82560340","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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