J. Mobile Multimedia最新文献

Augmented Reality-based Educational Content Application Development 基于增强现实的教育内容应用开发

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1945

Haekyung Chung, Jang-Hyok Ko

引用次数: 0

Hyperledger Fabric-based Reliable Personal Health Information Sharing Model 基于Hyperledger fabric的可靠个人健康信息共享模型

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1944

Jinsook Bong, Uijin Jang

引用次数: 0

Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions 基于Bi-LSTM的5G-OFDM无线网络在瑞利衰落信道条件下的研究

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1948

Sanjaya Kumar Sarangi, R. Lenka, Ravi Shankar, H. Mehraj, V. G. Krishnan

{"title":"Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions","authors":"Sanjaya Kumar Sarangi, R. Lenka, Ravi Shankar, H. Mehraj, V. G. Krishnan","doi":"10.13052/jmm1550-4646.1948","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1948","url":null,"abstract":"Fifth generation (5G) wireless networks’ system performance is dependent on having perfect knowledge of the channel state information (CSI). Deep learning (DL) has helped improve both the end-to-end reliability of 5G and beyond fifth generation (B5G) networks and the computational complexity of these networks. This work uses the Bi-linear long short-term memory (Bi-LSTM) scheme to examine the overall performance of the 5G orthogonal frequency division multiplexing (OFDM) technology. The least squares (LS) channel estimation scheme is a famous scheme employed to estimate the fading channel coefficients due to their lower complexity without the prior CSI. However, this scheme has an exceedingly high CSI error. Using pilot symbols (PS) and loss functions, this work has proposed the Bi-LSTM 5G OFDM estimators to improve the channel estimation obtained by the LS approach. All simulation analysis uses convex optimization (CO) software (CVX software) and stochastic gradient descent (SGD). When combined with many PS (72) and a cross-entropy loss function, the proposed Bi-LSTM outperforms the long-short-term memory (LSTM) cross-entropy, LS, and minimum mean square error (MMSE) estimators in low, medium, and high signal-to-noise ratio (SNR) regimes. The computational and training times of Bi-LSTM and LSTM DL estimators are also compared. Because of its DNN design, it can evaluate massive datasets, find hidden statistical patterns and characteristics, establish underlying relationships, and transfer what it has learnt to other contexts. Statistical analysis of the bit error rate (BER) reveals that Bi-LSTM outperforms the MMSE in terms of accurate channel prediction.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133268762","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}

引用次数: 0

Live Streaming Contents Influencing Game Playing Behavior Among Thailand Gamers 直播内容影响泰国玩家的游戏行为

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1946

Thanaphol Kongrit, S. Kiattisin

引用次数: 0

Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain 区块链安全与隐私智能合约交易验证的块哈希签名(BHS

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1941

S. Bhatnagar, M. Dayal, Deepti Singh, Shitiz Upreti, K. Upreti, J. Kumar

引用次数: 3

The Disruptive Innovation Potential and Business Case Investment Sensitivity of Open RAN 开放式RAN的颠覆性创新潜力与商业案例投资敏感性

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1943

T. Kyoseva, V. Poulkov, P. Lindgren

引用次数: 0

A Conceptual Model of Personalized Virtual Reality Trail Running Gamification Design 个性化虚拟现实越野跑游戏化设计的概念模型

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1947

Raslapat Suteeca, Smitti Darakorn Na Ayuthaya, S. Kiattisin

{"title":"A Conceptual Model of Personalized Virtual Reality Trail Running Gamification Design","authors":"Raslapat Suteeca, Smitti Darakorn Na Ayuthaya, S. Kiattisin","doi":"10.13052/jmm1550-4646.1947","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1947","url":null,"abstract":"Individuals’ running styles define trail running. Diverse motivational strategies for running emerge because of diverse running behaviors. Customizable design for motivation: when it comes to environmental considerations during a product’s or service’s use stage, the design process has become increasingly focused on behavior. Virtual reality enables the creation and integration of a variety of environments, as well as the redesign, retesting, and enhancement of these environments within a virtual computing structure. These benefits exist because players’ perspectives and behaviors in virtual environments are more comparable to those in their physical environments. The purpose of this study is to create a model for the relationship between persuasive strategy, user personal factors and target behavior that is effective based on the Social Cognitive Model, two hypotheses are tested using structural equation modelling (SEM). According to the findings, persuasive strategies have a significant positive influence on user personal factors. Second, user personal factors were able to influence target behaviors. To increase intrinsic motivation, virtual reality application designers should support persuasive tactics such as goal setting and self-monitoring in a target context. These results may guide designers in selecting effective persuasion strategies for various user groups.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132529652","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}

引用次数: 0

Protein Prediction using Dictionary Based Regression Learning 基于字典回归学习的蛋白质预测

J. Mobile Multimedia Pub Date : 2023-05-04 DOI: 10.13052/jmm1550-4646.1942

T. S. Rani, A. Babu, D. Haritha

{"title":"Protein Prediction using Dictionary Based Regression Learning","authors":"T. S. Rani, A. Babu, D. Haritha","doi":"10.13052/jmm1550-4646.1942","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1942","url":null,"abstract":"Research Objectives: Molecular genetic data is managed by the information technology known as bioinformatics. Major concept involved in bioinformatics is a protein sequence. Amino acids bonded with peptide bond constitute the sequence of Protein and it is very essential to lead life. To predict sequence of amino acid, primary sequence obtains amino sequence folding and structures prediction.\u0000Research Novelty: In this manuscript, dictionary based regression learning and fuzzy genetic algorithm is proposed for protein prediction from structural analysis (DRL-FGA-PD-SA). In this input data are taken from Kaggle domain dataset. The extraction of protein features from given data is made through Kernel Matrix (KM) which extracts composition of amino acids, composition of dipeptide, composition of pseudo-amino-acid, composition of functional domain and distance-based features. Then fuzzy based genetic algorithm (FGA) update the selected features for classification of protein and the features are clustered. Finally, dictionary based regression learning (DRL) predicts the class of protein with conversion of values either 0’s or 1’s.\u0000Research Conclusions: The proposed method is executed on MATLAB. Here evaluation metrics as sensitivity, precision, f-measure, specificity, accuracy and error rate are outlined. Then the performance of the proposed DRL-FGA-PD-SA method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, compared with the existing systems such assdeep learning methods in protein structure prediction (FFNN-RNN-PD-SA), deep learning technique for protein structure prediction and protein design (DNN-PD-SA) and improved protein structure prediction using potentials from deep learning (DNN-SGDA-PD-SA) respectively.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"105 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132901234","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}

引用次数: 0

Novel Deep Learning Approach to Support Optimal Resource Allocation in 5G Environment 支持5G环境下资源优化分配的新型深度学习方法

J. Mobile Multimedia Pub Date : 2023-02-15 DOI: 10.13052/jmm1550-4646.1935

Raja Varma Pamba, Rahul Bhandari, A. Asha, Rahul Neware, A. Bist

引用次数: 0

Multi-Class Classification Method with Feature Engineering for Predicting Hypertension with Diabetes 基于特征工程的多类分类方法预测高血压合并糖尿病

J. Mobile Multimedia Pub Date : 2023-02-15 DOI: 10.13052/jmm1550-4646.1937

Mongkhon Sinsirimongkhon, Sujitra Arwatchananukul, P. Temdee

{"title":"Multi-Class Classification Method with Feature Engineering for Predicting Hypertension with Diabetes","authors":"Mongkhon Sinsirimongkhon, Sujitra Arwatchananukul, P. Temdee","doi":"10.13052/jmm1550-4646.1937","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1937","url":null,"abstract":"Machine learning–based methods are widely applied for the prediction of noncommunicable diseases (NCDs), such as hypertension, diabetes, and cardiovascular disease. However, few models have been developed for predicting hypertension with diabetes, even though these diseases generally co-occur and can cause devastating harm to patients. This paper proposes a multi-class classification method that will be able to predict hypertension with diabetes. The proposed method consists of data preprocessing, model construction and validation, and model comparison. For data preprocessing, feature engineering of corresponding data types is conducted. For model construction, several machine learning methods are applied, including Random Forest (RF), Gradient Boosting (GB), Extra Tree (ET), Decision Tree (DCT), and Support Vector Machine (SVM). The dataset used in this study consists of 17,077 records and 28 features, obtained from Phaya Mengrai Hospital, Chiang Rai, Thailand. The predictive performance of each model with and without feature engineering is compared in terms of accuracy and average area under the Receiver Operating Characteristic curve (AUC-ROC). From the comparison results, SVM with feature engineering outperformed other models based on accuracy and average AUC-ROC achieving a value of 88.39% and 93.32%, respectively. For all ensemble learning–based methods, RF performed the best in terms of both accuracy and average AUC-ROC for both with and without feature engineering. Overall, all the models performed better when feature engineering was applied.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133736965","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}

引用次数: 1