• Energy Research

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER.

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student’s participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students’ performance.

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.