Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Stress Detection on Twitter: A Hybrid Approach using LSTM and Natural Language Processing

Stress is a common mental health problem that affects individuals from all walks of life. It can manifest in various forms, such as emotional, physical, or cognitive distress. The detection of stress in individuals is crucial for early intervention and treatment. However, traditional methods of stress detection, such as self-report surveys or physiological measures have limitations. In recent years, a social media platform like Twitter, have become a rich source of data for studying mental health. This study proposes the use of deep learning techniques, specifically the combination of LSTM and 1D CNN, to detect human stress levels from social media posts, particularly tweets. By leveraging Kaggle Twitter datasets alongside of the recent original Tweets, the model was well trained and validated, achieving high accuracy levels in stress detection.
Production Enhancement and Process Analysis for Pharmaceutical Industries

“Production Enhancement and Process Analysis for pharmaceutical industries” is a Pharma analytics process, which harnesses the power of data analytics to improve operations in the pharmaceutical industry. To provide recommendations to increase the production flow rate in the continuous process. Run-time recommendations and insights based on multivariate parameters to help operators achieve optimal outcomes. The process of working with different teams concerning their shifts working day and night is monitored. The fluctuations in the system help to investigate how each parameter is working in the plant individually and the best range of parameters to guarantee optimized production consistently are identified. In the existing system, the analysis was made unit-wise and batch-wise. Since it was not a continuous process, analysis was done batch by batch without any difficulties. Continuous manufacturing is favored because the results of one production run do not have an impact on the outcomes of subsequent runs, unlike batch manufacturing. Whereas in the proposed system, it was a continuous process which doesn’t contain any batches to overcome this challenge cycle time and lag time approach was used.
Faces are Domains: Domain Incremental Learning for Expression Recognition

Since most existing facial expression recognition methods depend on deep learning models trained in isolation on a facial expression image corpora, once employed in scenarios that are different from those in the corpora, they usually demand ad-hoc retraining to be able to perform better in the expression recognition task for new scenarios. Furthermore, most of these facial expression recognition methods are inconsistent when recognising person-specific expressions or are incapable of adjusting to real-world scenarios where data is exclusively obtainable incrementally. In this paper, we present a face incremental expression recognition model, where we utilise domain incremental learning methods to learn individual facial features of facial expressions. We assume that each individual's facial expression (domain) is presented to the model one domain at a time. We assessed our model's ability to remember previously seen domains (individual's facial expression) and incrementally perform on new face domains. Our model improves performance compared to a non-incremental learning model and an incremental learning model in facial expression recognition for individual data with different expression classes.
An Efficient Diabetes Prediction Model using Machine Learning

Diseases are the ones that affect the continual life of human beings. One such illness which affects millions of people worldwide is diabetes. By examining huge datasets of patients, machine learning models offer a viable answer to the problems associated with diabetes detection. The proper management of diabetes depends on early identification This article aims to assess the effectiveness of diverse machine learning methodologies and strategies utilized for forecasting diabetes, utilizing the PIMA Indian Diabetes dataset as a reference. The dataset contains information about crucial factors such as the patient’s age, BMI, blood pressure, and blood sugar levels, which are utilized for the analysis. The models which were taken into account in this research were compared to one another and then LightGBM was chosen as the primary model on the basis of its high accuracy. The Hyper parameters were then modified to produce the best performance possible. The results show that machine learning models can accurately detect diabetes and provide insights into the factors that contribute to the disease. This work provides a foundation for future research on diabetes prediction using machine learning models.
Entanglement criteria for the three-qubit states

We present sufficient criteria for the entanglement of three-qubit states. For some special families of states, the criteria are also necessary for the entanglement. They are formulated as simple sets of inequalities for the mean values of certain observables defined as tensor products of Pauli matrices. The criteria are good indicators of the entanglement in the vicinity of three-qubit GHZ and W states and enjoy the capability of detecting the entangled states with positive partial transpositions. Furthermore, they improve the best known result for the case of W state mixed with the white noise. The efficiency of the criteria is illustrated through several examples.
A Hybrid Impedance and Admittance Control Strategy for a Shape-Transformable Leg-Wheel

In this study, a hybrid impedance and admittance control strategy is developed as a low-level, high-speed controller for legged robots that fuses two controllers within the range of several ticks of a control loop. This strategy enables the rigid-link legs not only to have accurate motion trajectories but also to adapt to the impacts caused by interactions with unknown environments. In addition, owing to the significantly different characteristics of the leg in compression and in tension, the study introduces a novel switching strategy that adjusts the hybrid level of the controller in the range of the leg stride. The proposed strategy was experimentally validated on a linkage-based leg wheel, and the results confirm the effectiveness of the strategy.
AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence

Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.
A Standards-based Approach for Cross-Domain Modelling of Smart City System Architectures

The inherent complexity of systems in the domain of smart cities demands a suitable modelling framework to facilitate development and interdisciplinary communication. In the domain of smart grids, the Smart Grid Architecture Model (SGAM) framework provided means for coping with complexity whilst supporting a cross-disciplinary understanding of modelled systems. Similar approaches exist for the automotive and industry domains. As a complex smart city system may combine various application domains, interoperability between applied modelling frameworks is imperative to simplify cross-domain collaboration. Therefore, to be suitable, a modelling framework needs to target the concerns of stakeholders from the smart city, whilst simultaneously enabling interoperability between all connected application domains. The presented approach is a standards-based systems engineering framework, implemented as Domain Specific Language (DSL) and based upon the Smart City Reference Architecture Methodology (SCRAM). Moreover, the DSL is used to model a particular system solution to evaluate its applicability.
Deep Learning Model for Chronic Obstructive Pulmonary Disease through Breathing Sound

The lung condition known as Chronic Obstructive Pulmonary Disease (COPD) results in breathlessness, wheezing, or a persistent cough. Lack of oxygen causes the lungs to release extra carbon dioxide. The audio of the respiratory sounds is useful in the prediction of COPD since early detection is important for respiratory disorders. Initially, the diagnosis of COPD exacerbations has a lot of potential to benefit from deep learning approaches. Deep learning is an effective machine learning algorithm that combines supervised training with Convolutional Neural Network (CNN) and Long Short-Term Memory (LS TM) applications and unsupervised training to reduce optimization. The dataset of breathing sounds is from people with COPD and other six lung diseases, as well as those who are healthy. Feature extraction is done in order extract necessary features from the audio files. An artificial Convolutional Neural Network (CNN) was used to extract and classify features from the preprocessed dataset. Exacerbation forecasting could lessen their severe effects and the substantial expenditures related to COPD patients. Deep learning in breathing sounds is used to forecast whether an output will be COPD or non-COPD related. The findings demonstrate that the deep learning model was highly accurate in differentiating between people without COPD and those who have it. Also, this technique is a financially advantageous choice for long-term remote monitoring in medical applications.
Deep Learning based Analysis for Automated Detection and Classification of Brain Tumor

Reproduction of quick and indefinite cells within brain cause a tissue which is generally known as a brain tumor. A number of individuals remain untreated as it does not show any hard symptoms at an initial stage. For identification of such disease, many neurologists suggest Computer Tomography Scan, Magnetic Resonance Imaging etc. which can be time consuming process and expensive too. To avoid so, various computer assisted methods have been suggested by researchers to overcome the drawbacks of traditional approaches. Deep learning has been considered as one of the reliable approaches for identification and classification of brain tumor disease that can prevent an individual from death due to its strong features capability for providing quick and better results at an early stage as compared to the traditional approaches. This research study has considered 3264 images from kaggle having 2764 tumor images and 500 with healthy ones and proposed a model that comprises of Visual Geometry Group (VGG) having 16 layers in collaboration with the concept of transfer learning to perform the diagnosis and classification of brain tumor disease. The proposed model has delivered an accuracy of 98.16%, precision of 99.09%, recall of 98.73% and F1-score of 98.91% which is far better when compared to the existing approaches.
Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students

Application processes in higher education, particu-larly in fields like Computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (U CI), 57 % of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41 % entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demo-graphic and academic performance variables for CS students at U CI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector Machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.
Specificity of nursing postoperative care for patients after kidney transplantation in the early period

W artykule omówiono zagadnienie opieki pielęgniarskiej nad pacjentem we wczesnym okresie po przeszczepie nerki. Przedstawiono w nim komponenty opieki pielęgniarskiej nad chorym po przeszczepie nerki oraz wskazano na ich zasadność. Zaakcentowano znaczenie systematycznych pomiarów parametrów życiowych pacjenta wraz z ciągłą i dokładną oceną diurezy chorego. Poruszono także zagadnienie rozmowy, którą pielęgniarka przeprowadza z chorym w okresie potransplantacyjnym, oraz zasygnalizowano problem opieki nad chorym, u którego nastąpił odrzut przeszczepionego organu.
Level set topology optimization of structural problems with interface cohesion

This paper presents a finite element topology optimization framework for the design of two‐phase structural systems considering contact and cohesion phenomena along the interface. The geometry of the material interface is described by an explicit level set method, and the structural response is predicted by the extended finite element method. In this work, the interface condition is described by a bilinear cohesive zone model on the basis of the traction‐separation constitutive relation. The non‐penetration condition in the presence of compressive interface forces is enforced by a stabilized Lagrange multiplier method. The mechanical model assumes a linear elastic isotropic material, infinitesimal strain theory, and a quasi‐static response. The optimization problem is solved by a nonlinear programming method, and the design sensitivities are computed by the adjoint method. The performance of the presented method is evaluated by 2D and 3D numerical examples. The results obtained from topology optimization reveal distinct design characteristics for the various interface phenomena considered. In addition, 3D examples demonstrate optimal geometries that cannot be fully captured by reduced dimensionality. The optimization framework presented is limited to two‐phase structural systems where the material interface is coincident in the undeformed configuration, and to structural responses that remain valid considering small strain kinematics. Copyright © 2017 John Wiley & Sons, Ltd.
Experimental Comparison of STR and PI Controllers on a Nonlinear Liquid-Level Networked Control System

This paper discusses the design, practical implementation, and comparison of the adaptive controller using the indirect self-tuning regulator (STR) method and PI controller for a liquid-level networked control system. The system model is identified by using the least squares (LS) method and design a PI controller tuned by Ziegler-Nichols method. Also, the system parameters are estimated online, using the recursive least squares (RLS) method to implement the indirect STR adaptive controller. With the help of the estimated parameters, the STR controller should be designed through pole placement. To compare the performance of the controllers, the dynamics of the liquid-level control system have been changed by placing an external heterogeneous object inside the tank. Adaptive controllers are used in systems with variable parameters to detect dynamic changes and control the system appropriately. The results show that the indirect STR adaptive controller performs better than the PI controller in both transient and steady-state responses.
Automatic Image Compositing and Snow Segmentation for Alpine Snow Cover Monitoring

Accurate snow cover maps are an important tool in a large variety of applications including avalanche research. The WSL Institute for Snow and Avalanche Research (SLF) has developed a snow cover mapping system for the Dischma valley in Davos, based on ground-based cameras. Their goal has been to validate snow-cover maps derived from satellite data. In the currently implemented system, several steps require manual work. The goal of this paper is to automate SLF’s approach by applying a deep learning framework. For the training of our models, we have access to data from multiple cameras mounted in the Dischma valley. In our approach, we first apply deep learning to perform fog classification and then to do pixel-wise snow segmentation. Unlike the current procedure our method is independent of image- and camera-specific thresholds. In our experiments, we compare a model that is trained on images from all cameras to camera-specific models for both tasks. For the fog classification, we aim for a higher recall to be able to detect most of the foggy images. We show that, when tuned to achieve the same precision as the baseline, our model improves recall by 17%. For the snow segmentation, we evaluate our models based on the Fl-score. When using our fully automated machine learning model without any manually selected thresholds, we achieve a higher Fl-score only for a part of the cameras in the valley, and on average, this comes at a cost of 2.3%. We also show that for the case of a new camera being mounted in the Dischma valley, our models for both tasks can still be used for that new camera and will achieve similar results compared to the currently mounted cameras.
Prediction of Stock Price by using Machine Learning & Big Data Approaches

Today, social networks are extremely important to every member of society, regardless of their age, community, gender, or professional background. Social networks already connect between 3.4 and 3.6 billion individuals, and by 2025 that number is expected to rise to roughly 4.50 billion. The widespread use of social media platforms makes it possible for numerous e-commerce businesses to spur growth by focusing on certain customer segments. However, it is difficult for businesses to connect with their target audiences because they need to examine enormous amounts of data produced in a social network of people from different age groups and origins. Big Data, which manages both structured and unstructured data, steps in to handle the analysis of these enormous volumes of data. On the basis of the consumer’s behavior that satisfies their needs, an algorithm that assesses and divides them is required to be implemented on these data. Two mechanisms are used in this research to offer an ideal answer to this issue. In the first, Word2vec is applied to information that offers the words’ numerical representations (vectors), and in the second, K-Means Clustering is used to the numerical values to create a cluster of related words. Companies can determine which stock is more desired or liked by the public by using such clusters. As a result, it is thought that this approach offers businesses a solid resolution.
Comparative Analysis of 51-level Hybrid Multilevel Inverter and 57-level Switched-capacitor Boost Multilevel Inverter

The invention of hybrid multilevel inverters (HMI) and switched-capacitor boost multilevel inverters (SCBMI) has revolutionized power electronic systems by enabling the generation of extremely high AC voltage-level using asymmetrical DC configurations. This paper presents the comparison of the 51-level HMI and 57-level SCBMI topologies in terms of the total number of components and the total harmonic distortion (THD) across different modulation indices (i.e., 0.2, 0.4, 0.6, 0.8, and 1). In addition, the effective switching frequency and the fundamental frequency component of the AC voltage waveforms were also compared. Both the presented inverter topologies were driven by the nearest level control (NLC) modulation technique under one resistive-inductive RL loading condition. MATLAB Simulink simulation studies are utilized to validate the theoretical study of the differences between the topologies.
Comparing Privacy Label Disclosures of Apps Published in both the App Store and Google Play Stores

Apple and Android introduced privacy labels in 2020 and 2022 respectively as a way of providing consumers with succinct summaries of mobile apps’ more salient data practices. A number of apps are published in both stores, offering us the opportunity to compare their privacy label disclosures in the two app stores. This paper compares the data practices privacy labels are intended to capture in each store. It then proceeds to analyze the disclosures of 822 apps published in both app stores, focusing on possible discrepancies. This analysis reveals that privacy label disclosures of what is ostensibly the same mobile app can be quite different. We discuss the different possible reasons behind these differences, including the possibility that these discrepancies might be indicative of potential privacy compliance issues. In particular, focusing on data collection disclosures of five different data types (location, contact info, sensitive info, identifiers, and health & fitness) we find discrepancies between iOS and Google Play privacy label disclosures in 66.5% of the mobile apps we analyze.
Remaining Useful Life Prediction Method for the Rolling Element of an Electrical Machine Using Linear Regression Analysis of the Vibration Signal of a Faulted Bearing

The anticipation of potential failures and provision of early warning signals are enabled by predictive maintenance, playing a vital role in ensuring the optimal performance and reliability of electromechanical systems. In this context, the research presents an effective and efficient approach for predicting bearing faults, focusing on the analysis of vibration signals from rolling elements, particularly bearings. By applying linear regression analysis, the vibration signal from each bearing sample is transformed into the frequency domain, enabling the calculation of the area under the curve. To estimate the remaining useful life (RUL) of the bearing, the research utilizes linear regression analysis, where the slope of the regression line serves as a crucial indicator. A positive slope suggests accelerated wear or imminent failure, indicating a decrease in the RUL as the independent variable increases.By proactively detecting and resolving potential faults, industries can effectively minimize costs linked to unexpected downtime, urgent repairs, and component replacements. Notably, the study utilizes benchmark data sourced from the NASA prognostics data archive.