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Article Citation - WoS: 18Citation - Scopus: 24Fusion of Smartphone Sensor Data for Classification of Daily User Activities(Springer, 2021) Sengul, Gokhan; Ozcelik, Erol; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisNew mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.Article Citation - WoS: 38Citation - Scopus: 53Outdoor Path Loss Predictions Based on Extreme Learning Machine(Springer, 2018) Popoola, Segun I.; Misra, Sanjay; Atayero, Aderemi A.In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network Back-Propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura-Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively; and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively; and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast training convergence, high prediction accuracy, and good generalization ability for radio network planning and optimization in outdoor environments.Article Citation - WoS: 3Citation - Scopus: 3Impact of Mobile Received Signal Strength (rss) on Roaming and Non-Roaming Mobile Subscribers(Springer, 2023) Karanja, Hinga Simon; Misra, Sanjay; Atayero, A. A. A.Mobile phones have transitioned from voice-centric devices to smart devices supporting functionalities like high-definition video and games, web browsers, radio reception, and video conferencing. Mobile phones are used in telemedicine, health monitoring applications, navigation tools, and gaming devices, among other applications. Given the above, Mobile broadband connectivity affects mobile access to the internet and voice communications. This paper assesses the impact of the Reference Signal Received Power (RSRP) and broadband connectivity around Covenant University. LTE, GSM, and HSPA mobile signal measurement campaigns were conducted around Covenant University in Ota, Ogun state, Nigeria. To investigate the best optimized mobile network for mobile subscribers on roaming services and subscriber's high performance and data rates. After the experiment, exploratory data analysis was used to visualize the best mobile network; GSM proved as stable than LTE and HSPA.Article Citation - WoS: 2Citation - Scopus: 4Ronsi: a Framework for Calculating Return on Network Security Investment(Springer, 2023) Barik, Kousik; Misra, Sanjay; Fernandez-Sanz, Luis; Koyuncu, MuratThis competitive environment is rapidly driving technological modernization. Sophisticated cyber security attacks are expanding exponentially, inflicting reputation damage and financial and economic loss. Since security investments may take time to generate revenues, organizations need more time to convince top management to support them. Even though several ROSI techniques have been put out, they still need to address network-related infrastructure. By addressing gaps in existing techniques, this study delivers a comprehensive framework for calculating Return on Network Security Investment (RONSI). The proposed framework uses a statistical prediction model based on Bayes' theorem to calculate the RONSI. It is validated by Common Vulnerability Security Systems (CVSS) datasets and compared to existing studies. The results demonstrate that the annual loss is reduced to 75% with the proposed RONSI model after implementing a security strategy, and the proposed model is compared with existing studies. An organization can effectively justify investments in network-related infrastructure while enhancing its credibility and dependability in the cutthroat marketplace.Article Citation - WoS: 37Citation - Scopus: 61Adoption of Mobile Applications for Teaching-Learning Process in Rural Girls' Schools in India: an Empirical Study(Springer, 2020) Chatterjee, Sheshadri; Majumdar, Dipasree; Misra, Sanjay; Damasevicius, RobertasThe purpose of this study is to identify the factors that can impact the adoption of mobile apps for teaching-learning process focusing on the girls' school in rural India. The hypotheses were proposed and a conceptual model has been developed. There is a survey work conducted to collect the data from different respondents using a convenience sampling method. The model has been validated statistically through PLS-SEM analysis covering feedbacks of 271 effective respondents. The study highlights the impact of different antecedents of the behavioural intention of the students of using mobile applications for teaching-learning process. The results also show that among other issues, price value has insignificant influence on the intention of the girl students of the rural India. During survey feedbacks have been obtained from the 271 respondents, which is meagre compared to vastness of the population and school of rural India. Only few predictors have been considered leaving possibilities of inclusion of other boundary conditions to enhance the explanative power more than that has been achieved in the proposed model with the explanative power of 81%. The model has provided laudable inputs to the educational policy makers and technology enablers and administrators to understand the impact of the mobile applications on the rural girls' school of India and facilitate the development of m-learning. Very few studies been conducted to explore the impact of mobile applications on the school education of rural India especially focusing on the girls' schools.Article Citation - WoS: 2Citation - Scopus: 17Determining Suitability of Speech-Enabled Examination Result Management System(Springer, 2019) Azeta, Ambrose A.; Misra, Sanjay; Azeta, Victor I.; Osamor, Victor ChukwudiThe focus of this study is to determine the suitability of speech-enabled examination result management system as a tool for checking and managing students' examination results. The theory of task-technology fit was used to identify the factors that significantly influence the use of the system. 374 verified data from students and instructors that responded to the questionnaire were analyzed and reported. The factors investigated in this study were cost, task, mobility, attitude, fitness, performance and utilization. Structural equation modeling was engaged to study the relationship between the variables and also analyze the data. The outcome of the constructed model proved that mobility, task and cost had significant influence on the fitness of the system.
