Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications最新文献

{"title":"Behavioural Analysis of Deaf and Mute People Using Gesture Detection","authors":"Nirmala M.S.","doi":"10.58346/jowua.2023.i3.010","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.010","url":null,"abstract":"Deaf and mute people have unique communication and social challenges that make it hard to express their thoughts, needs, and ideas. Understanding people's behavior is more important to protect them and help them integrate into society. This study discusses the critical need for behavioral analysis on deaf and mute people and introduces the Automatic Behavioral Analysis Employing Gesture Detection Framework (ABA-GDF). Gesture detection technology has gained popularity recently. This emphasis may be due to its ability to overcome communication hurdles and illuminate nonverbal communication. Current methods have various challenges, including limited accuracy and adaptability. The ABA-GDF architecture comprises three phases: dataset collection, modeling, and deployment. The data collection technique includes hand signals used by deaf and quiet people. The material is then processed to partition and normalize the hand area for consistent analysis. During Modelling, feature descriptor attributes are developed to extract relevant motion information. A classifier learns and predicts using the feature vectors, enabling the framework to recognize and interpret motions and actions. Large-scale simulations of ABA-GDF showed promising results. The ABA-GDF framework achieved 92% gesture recognition accuracy on the dataset. The system's robustness is demonstrated by its capacity to understand non-verbal messages. The research showed a 15% reduction in false positives compared to earlier methods, demonstrating its real-world usefulness.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"98 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":"135038805","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":"Multidirectional Trust-Based Security Mechanisms for Sinkhole Attack Detection in the RPL Routing Protocol for Internet of Things","authors":"Sopha Khoeurt, Chakchai So-In, Pakarat Musikawan, Phet Aimtongkham","doi":"10.58346/jowua.2023.i3.005","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.005","url":null,"abstract":"The Internet of Things (IoT) has gained popularity in recent years by connecting physical objects to the Internet, enabling innovative applications. To facilitate communication in low-power and lossy networks (LLNs), the IPv6-based routing protocol for LLNs (RPL) is widely used. However, RPL’s lack of specified security models makes it vulnerable to security threats, particularly sinkhole attacks. Existing sinkhole attack detection techniques suffer from high detection delays and false positives. To overcome these limitations, in our research we propose a multidirectional trust-based detection approach for sinkhole attacks in the RPL routing protocol. Our model introduces a novel architecture that considers trust in parent, child, and neighbor directions, reducing detection delays. We enhance detection efficiency and reduce false positives by combining fuzzy logic systems (FLSs) and subjective logic (SL). Additionally, we introduce a new trust weight variable derived from Shannon's entropy method and multiattribute utility theory. We adaptively adjust the SL coefficient based on network conditions, replacing the constant coefficient value of SL theory. Our approach is compared to the most recent techniques, and we assess different indicators, such as false-positive rate, false-negative rate, packet delivery ratio, throughput, average delay, and energy consumption. Our results demonstrate superior performance in all these metrics, highlighting the effectiveness of our approach.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"2014 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":"135039770","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":"Data Optimization using PSO and K-Means Algorithm","authors":"Abdul Rahmat, Ahmad Arif Nurrahman, Susatyo Adhi Pramono, Dadi Ahmadi, Winci Firdaus, Robbi Rahim","doi":"10.58346/jowua.2023.i3.002","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.002","url":null,"abstract":"Tourism is one of the industries that contribute considerably to the country's economy. Tourism helps the country's economy expand by providing and increasing jobs, living standards, and triggering the rise of other tourist-related production. The tourism industry will become a multinational industry and the primary driver of the global economy in the twenty-first century. Tourism has generated significant foreign exchange for a number of countries. Indonesia, the world's biggest archipelagic country with 17,508 islands, often known as the archipelago or maritime country, has recognized the importance of the tourist sector to the Indonesian economy because tourism growth consistently outpaces economic growth. The research's goal is to map the number of tourist visits. The mapping is in the form of clusters based on countries. The technology utilized is classification data mining with the K-Means method and Particle Swarm Optimization (PSO). The dataset came from the Central Bureau of Statistics, a government organization (abbreviated as BPS). The research outcomes in cluster mapping, with the cluster results compared to standard K-Means and K-Means + PSO. RapidMiner software is used during the analytical process. The calculation results in the form of clusters will be evaluated using the Davies-Bouldin Index (DBI) parameter. The cluster value (k) used is k = 2, 3, 4, 5. The findings show that the K-Means + PSO optimization has the minimum DBI value for k = 5. Meanwhile, the DBI value for k = 5 is 0.134.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"2 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":"135039629","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":"Semantic Annotation Based Mechanism for Web Service Discovery and Recommendation","authors":"J. Brindha Merin, Dr.W. Aisha Banu, Akila R., Radhika A.","doi":"10.58346/jowua.2023.i3.013","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.013","url":null,"abstract":"Web Mining is regarded as one among the data mining techniques that aids in fetching and extraction of necessary data from the web. Conversely, Web usage mining discovers and extracts essential patterns usage over the webs which are being further utilized by various web applications. In order to discover and explore web services that are registered with documents of Web Services-Inspection, Discovery and Integration registry, Universal Description wants specific search circumstance similar to URL, category and service name. The document of Web Service Description Language (WSDL) offers a condition of the web services customers to take out operations, communications and the service format of right message. Therefore, WSDL is being utilized together with semantic explanation dependent substantiation for the extraction of different web services for related purpose, other supporting operations and attributes. The reason is that there subsist different web services having corresponding functionalities however altered or changeable attributes that are non–functional. Resultant, recognize the preeminent web service become tiresome for the user. A method is projected which caters the analysis of service resemblance with the aid of semantic annotation and machine learning (ML) algorithms depending on the analysis intended for enhancing the classification through capturing useful web services semantics related with real world. The emphasizes on the research technique of choosing preeminent web service for the user based on the semantic annotation. The research work in turn recommends a web mining technique that determines the best web service automatically thus ranking concepts in service textual documentation and classifies services on behalf of particular domains. Parallel computation is made easy with web services. The different management stages in the system of recommendation entail collection of dataset through WSDL on the semantic annotation basis, thereby recognizing the best service with the DOBT-Dynamic operation dependent discovering method, ranking through mechanisms MDBR - Multi-Dimensional based ranking, recommendation and classification. In this work, it has been employed a combination of fundamental ML estimators, namely Multinomial Naive Bayes (MNB) and Support Vector Machines (SVM), as well as ensemble techniques such as Bagging, Random Forests, and AdaBoost, to perform classification of Web services. It was observed from the investigate work that the adapted system of best web services recommendation defers high performance in contradiction of the existing recommendation technique regarding accuracy, efficiency in addition to processing time.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"98 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":"135039022","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":"Efficient Hybrid CNN Method to Classify the Liver Diseases","authors":"Venugopal Reddy Modhugu","doi":"10.58346/jowua.2023.i3.004","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.004","url":null,"abstract":"This study focuses on classifying liver diseases using dynamic CT scan images and deep learning techniques. The primary objective is to develop accurate and efficient models for distinguishing between different liver disease categories. Three deep learning models, ResNet50, ResNet18, and AlexNet, are employed for three-class classification, including Hepatitis/cirrhosis, Hepatitis/Fatty liver, and Hepatitis/Wilson's Disease. The dataset comprises dynamic CT scan images of the liver, each manually segmented to identify lesions. To enhance model performance, the data is pre-processed by resizing, normalization, and data augmentation. The dataset is split into training, validation, and test sets for model evaluation. The performance of each model is assessed using confusion matrices, accuracy, sensitivity, and specificity. Results show varying accuracies for different liver disease classes, indicating the strengths and limitations of the models. To overcome the limits of the three-class classifiers, a framework for the Efficient Hybrid CNN method to classify Liver diseases (EHCNNLD) is proposed, combining the predictions from the three models with weighted probabilities. The Proposed EHCNNLD method demonstrates improved accuracy and classification power, enhancing the overall performance for liver disease classification. The study highlights the potential of deep learning techniques in medical image analysis and clinical diagnosis. The findings provide valuable insights into developing robust and accurate models for liver disease classification, paving the way for medical research and patient care advancements.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"36 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":"135039627","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":"Availability of Voice-Recognition Devices to Support Visually Impaired Students in Saudi Arabian Universities","authors":"Dr. Yusra Jadallah Abed Khasawneh, Dr. Mohamad Ahmad Saleem Khasawneh","doi":"10.58346/jowua.2023.i3.014","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.014","url":null,"abstract":"This study investigates the availability of instructing visually impaired students utilizing voice-recognition devices in universities in Saudi Arabia. The study also compares the learning encounters of understudies both recently and after the consolidation of such innovation into their instruction. The descriptive approach was utilized for the reason of depiction in this investigation. The study used observation and interviews to collect data, which was gathered from 50 participants. The study found that an unfinished program, a long learning process for the console and images, and a deficiency of qualified computer voice integration are all things that work against the device's guarantee and hold it back from coming to its full potential. Students’ degrees of excitement might change anyplace from typical to exceptional. The educational modules will be presented in stages, depending on each student's current skill level. It is accepted that students' ability to form viable utilize of computers within the learning process will proceed to make strides as a result of the expanding number of intercessions that are getting to be open.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"37 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":"135039766","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":"Strengthening IoT Intrusion Detection through the HOPNET Model","authors":"Chandrababu Majjaru, Senthilkumar K.","doi":"10.58346/jowua.2023.i3.007","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.007","url":null,"abstract":"The rapid growth of Internet of Things (IoT) applications has raised concerns about the security of IoT communication systems, particularly due to a surge in malicious attacks leading to network disruptions and system failures. This study introduces a novel solution, the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks. Validation using the Nsl-Kdd dataset involves meticulous data preprocessing for error rectification and feature extraction across diverse attack categories. Implemented on the Java platform, the HOPNET model undergoes comprehensive evaluation through comparative analysis with established intrusion detection methods. Results demonstrate the superiority of the HOPNET model, with improved attack prediction scores and significantly reduced processing times, highlighting the importance of advanced intrusion detection methods for enhancing IoT communication security. The HOPNET model contributes by establishing robust defense against evolving cyber threats, ensuring a safer IoT ecosystem, and paving the way for proactive security measures as the IoT landscape continues to evolve.","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":"135038466","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":"Human Activity Recognition Using Ensemble Neural Networks and The Analysis of Multi-Environment Sensor Data Within Smart Environments","authors":"Akila R., J. Brindha Merin, Radhika A., Dr. Niyati Kumari Behera","doi":"10.58346/jowua.2023.i3.016","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.016","url":null,"abstract":"The significant focus and potential value of Human Activity Recognition (HAR) technologies based on non-invasive ambient sensors have been attributed to the advancement of Artificial Intelligence (AI) and the widespread adoption of sensors. Due to the proactive engagement of human activities and the utilization of Machine Learning (ML) techniques that depend on domain expertise, developing a standardized model for comprehending the everyday actions of diverse individuals has significant challenges. A technique for recognizing the user's everyday activities in multi-tenant intelligent environments has been developed. This methodology considers data feature limits and recognition approaches and is designed to limit sensor noise during human activities. This work aims at enhancing the quality of a publicly accessible HAR dataset to facilitate data-driven HAR.Additionally, the paper proposes a novel ensemble of neural networks (NN) as a data-driven HAR classifier. A Spatial Proximity Matrix (SPM)uses ambient sensors to facilitate contextawareness and mitigate data noise. The proposed method, named Homogeneous Ensemble Neural Network and Multi-environment Sensor Data (HENN-MSD), leverages a combination of a homogeneous ensemble NN and multi-environment sensor data to identify what individuals do in daily life accurately. The study featured the generation and integration of four fundamental models using the support-function fusion approach. This method included the computation of an output decision score for each basis classifier. The analysis of a comparative experiment conducted on the CASAS dataset indicates that the proposed HENN-MSD technique exhibits superior performance compared to the state-of-the-art methods in terms of accuracy (96.57%) in HAR.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"41 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":"135039763","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":"Enhancing User Experiences in Ubiquitous Soft Computing Environments with Fuzzy Agent Middleware","authors":"V. Naren Thiruvalar, Yamini R., Manimekalai Dr.M.A.P., Dr. I Wayan Suryasa, Sugapriya S.","doi":"10.58346/jowua.2023.i3.003","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.003","url":null,"abstract":"In the Ubiquitous Computing Environment (UCE) context, the successful provision of user-required services necessitates the collaboration of various system components, encompassing hardware parts, software components, and network connections. The utilization of UCE has presented some difficulties to customers seeking services in a diverse environment, including excessive Memory Usage (MU) and prolonged Component Construction Time (CCT). To optimize the user's experience, fuzzy agent employs a non-intrusive approach in online deep-rooted learning to get insights into user behavior. Integrating the advancements mentioned above aims to enhance the connectivity between users and information technology devices by utilizing an invisible network of UCE devices, creating dynamic computational environments that can effectively meet the users' needs. This work presents a unique methodology called Fuzzy Agent Middleware to Enhance User Experiences (FAM-EUI), which aims to improve user experiences in contexts where computer technology is seamlessly incorporated into everyday activities. This research endeavors to tackle the issues associated with imprecise data and enhance user-friendly interactions by integrating fuzzy logic with intelligent agents. The results highlight the potential of FAM in enhancing user interaction within ubiquitous soft computing, leading to improved efficiency and user-centered computing systems. This study provides valuable insights into integrating soft computing and agent-based technologies to enhance ubiquitous computing paradigms.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"43 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":"135039769","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":"Quantitative Evaluation of Android Application Privacy Security Based on Privacy Policy and Behaviour","authors":"Alcides Bernardo Tello, Sohaib Alam, Archana Ravindra Salve, B.M. Kusuma Kumari, Meena Arora","doi":"10.58346/jowua.2023.i3.019","DOIUrl":"https://doi.org/10.58346/jowua.2023.i3.019","url":null,"abstract":"The emerging landscape of Android applications in mobile phones encompasses industries involving millions and billions of app developers to improve usability and comfort for smartphone users. Popular apps are categorized into social media, entertainment, games, news, lifestyle, health and fitness. In this vein, privacy security is categorized into two major sections: distribution and development of particularities and running software on the user's mobile. According to European Union, the major issues of legal and regulations arising from the requirement from the GDPR, the Personal information protection law of China and other related regulations combined with the behaviour and privacy policy of application. In this article, Privacy security was quantitatively analyzed through data collection and analysis of scores by comparing the comprehensive use of ML, NLP and other technologies. E-privacy was regulated in the environment of mobile applications. The features were analyzed in privacy and data protection. The scope of this study is to evaluate the privacy security of the application in Android devices based on the privacy policy and behaviour.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"130 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":"135081317","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}