Miracle-dz/UniFAR-FTUs huggingface dataset:
Haptic Based Surface Texture Classification Using Machine Learning Techniques

This paper explores the application of machine learning techniques for object classification based on the haptic sensation of surface textures. The haptic sensation of different surfaces was recorded using an actuator as the position and reaction force response variations. The reaction force from the surface was obtained through an estimation using sensorless sensing technique of reaction force observer. Using the gathered data for five different surface classes, feature extraction was done and feature correlation was identified. A Random Forest (RF) classifier model and a Convolutional Neural Network (CNN) model were trained using selected features. Through testing and measuring performances, it was identified that the CNN model outperformed the RF model in terms of accuracy and speed. However, for training the CNN model requires a high amount of computational resources. Despite this drawback, the CNN algorithm is apparently the most suitable machine learning model for this application. This approach has potential applications in industrial processes such as quality assurance and wear and tear assessment.
A Hierarchical Approach for Energy-Aware Distributed Embedded Intelligent Video Surveillance

Intelligent video surveillance (IVS) offers a large spectrum of different applications that have strict requirements on quality of service (QoS) and energy-efficiency. Recent embedded IVS systems need to deliver compressed video data in high quality while using devices that are partly solar- or battery-powered. In this paper we present PoQoS, a novel hierarchical approach for combined power- and QoS-management in distributed embedded IVS systems. PoQoS allows the implementation of both global and local power- and QoS-adaptation in order to achieve optimal energy/QoS tradeoffs. Furthermore, we present a DSP-based, embedded platform that is used for IVS. It includes QoS-triggered onboard dynamic power management (DPM) that is controlled via Ethernet. We demonstrate the feasibility of PoQoS with a typical IVS-setup and present experimental results
Exploring the relationship between
color and form in the design of women’s Latin dance costumes

This article delves into the complex world of designing women's Latin dance costumes, highlighting the paramount importance of harmonizing current fashion trends with the essential requirements of comfort, flexibility, and the need to emphasize the dancer's graceful movements. The objective of this study was to create few models of Latin dance costumes. To forecast how the users and viewers would perceive Latin dance outfits, an on-line survey was conducted. A regression model was applied to analyze how color and shape interact in the creation of Latin dance costumes. The article focuses on key aspects of costume design, covering an in-depth study of silhouettes and colors and their perception by viewers. Designers are encouraged to focus their creative efforts on prioritizing the comfort and freedom of movement of the dancers, adapting to ever-evolving societal and cultural changes, and exploring innovative solutions to overcome the challenges and limitations inherent in costume creation. The regression model can be used to predict with enough accuracy users' and viewers' opinions on the use of Latin dance costumes. Embracing these results, designers are ready to produce remarkable, comfortable clothing patterns that authentically capture the splendor and diversity of Latin dance culture.
Detection of fault lineaments of the Zagros fold-thrust belt based on Landsat imagery interpretation and their spatial relationship with Hormoz Series salt dome locations using GIS analysis

In the Zagros structural zone of Iran, there is a broad range of emergent salt domes, a unique morphology in the world that is developed in tectonic environment. In the Zagros fold-thrust belt, emergent salt domes exhibit linear patterns. Such trends appear to be related to linear structural features that are observable by Landsat imagery. Detection of fault lineaments and their spatial relationship with salt dome locations from statistics point of view have been the concerns of this study. Based on the remote sensing analyses, horizontal displacements of parts of folded structures and distribution of Hormuz series salt domes, 34 fault lineaments were detected. A statistical method called the weight of evidence method is used to determine the relationship between salt dome locations and fault lineament trends. For this purpose, a map of the fault lineaments and a map of the salt dome centers were converted to raster model and some buffers were extracted around the fault lineaments; then, the spatial relationship between the salt domes and fault lineaments were quantified using weights of evidence method. Results indicate that the salt domes are associated spatially with the fault lineaments within lateral distances of 1 km. Weights of evidence method shows that the fault lineaments related to tectonics of the region could be the most important factor in emplacement of the salt domes in the study area.
The effects of pediatric flat foot on the frontal alignment of proximal segment

: The purpose of this study was to determine the effects of flat foot on the frontal alignment of calcaneus and tibia in 23 children with flat feet, aged 7-9 (7.43±0.58 years old). To evaluate the foot, the Foot Posture Index-6 (FPI 6) was used, while the posture of the calcaneus and the tibia was analyzed using the Global Postural System 400. The statistical results indicate strong intensity links between flat foot and the frontal alignment of the calcaneus and the tibia, as follows: FPI 6 right foot score and calcaneus - right foot angle (r= 0.824; p < 0.001), tibia - right foot angle (r= 0.588; p < 0.001); FPI 6 left foot score and calcaneus - left foot angle (r= 0.824; p < 0.001), tibia - left foot angle (r= 0.853; p < 0.001). Our study demonstrates that pediatric flat foot has the capacity to significantly influence the values of the angle of the calcaneus and tibia in a frontal plane.
A Fault-Tolerant Load Balancing Approach Using Whale Optimization Algorithm for Cloud-Fog Based IoT Applications

Cloud computing provides storage and computation for on-demand IoT (Internet of Things) usage. The huge requests of IoT device create network congestion and late response. The real-time and delay-sensitive requests need fast processing which can be provided by fog computing closer to network edge. A compound cloud-fog model can manage both real-time and delayed requests. The possibility of distribution of real-time requests among fog nodes arises uneven load distribution which increases with reassignment of requests of fault nodes. Hence a fault tolerant load balancing algorithm based on whale optimization algorithm is proposed that can evenly distribute the load of resources of fog node optimally among both fresh requests and requests coming from faulty nodes. The metrics as makespan and percentage of successful completion of requests are evaluated and validated with other algorithms. In addition to this an IoT hardware setup is designed and checked outcomes of the metrics.
DiMANI: diffusion MRI for anatomical nuclei imaging—Application for the direct visualization of thalamic subnuclei

The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.
Information Extraction From Digital Receipts and Bank Transactions Using Machine Learning

This paper presents a machine learning solution so as to automate extraction of key information from submitted bank transaction portable document file (pdfs) such as (National Electronic Funds Transfer) NEFT acknowledgements and digital transaction receipts. The paper proposes a pipeline to preprocess the data sample to improve Tesseract's performance for text extraction. Key information extraction from digital receipts is implemented using two approaches namely the CUTIE model, a CNN model exploiting both spatial and semantic information and an objection detection model. Information extraction from bank transaction acknowledgements is achieved by pandas dataframe manipulation coupled with substring search and Fuzzy string matching. The cross-entropy loss function for the CUTIE model converges at 0.4. The F1 scores of the object detection model was 0.92 for amount class and 0.87 for other classes respectively.
Antenna Shape Modeling based on Histogram of Oriented Gradients Feature

A least square support vector machine (SVM) model is proposed for shape modeling of slot antennas. The slot image is mapped into the electromagnetic response by the SVM model. A modified shape-changing technique is also proposed to describe the antenna geometry by the quadratic uniform B-spline curve and generate the slot images. In the model, the histogram of oriented gradients feature is extracted from the slot images to show the appearance and shape of the slot. The relationship between the histogram of oriented gradient features and the electromagnetic responses is preliminarily built on SVM and the transfer function. Then a radial basis function network is used for error correction. The effectiveness of the proposed model is confirmed with an example of a tri-band microstrip-fed slot antenna. Compared with the convolutional neural network (CNN), the feature extracted by CNN is substituted by the histogram of oriented gradients feature, and the proposed model shows the same accuracy and the improvement of training efficiency.
A Novel Hybrid Model for Predictive Analysis of Myocardial Infarction using Advanced Machine Learning Techniques

Cardiovascular disease (CVD) remains a leading cause of mortality, posing challenges for early detection and prediction. The recent survey of interest among researchers focuses on advanced machine-learning (AML) models due to their impressive precision, accurate classification, and predictive capabilities. This area has particularly significant implications within medical cardiology, as it aims to promptly identify CVD. This study introduces an effective and precise system for detecting Myocardial Infarction (MI). The system leverages three distinct feature selection approaches-filter methods, wrapper methods, and embedded methods—in conjunction with eight ML algorithms: logistic regression (LR), k-nearest neighbors(KNN) classifier, support vector classifier (SVC), decision tree (DT), random forest (RF), gradient boosting (GB), ada boost (AB) classifier, and xgb classifier based on the performance compare all. Incorporating these methods enhances the classification model’s performance while reducing computational complexity. The proposed model is evaluated using a standardized dataset, demonstrating superior predictive capabilities in terms of accuracy, sensitivity, precision, f1-score, auc, and specificity. This novel approach outperforms existing research in this domain, further underscoring its potential in advancing MI prediction and diagnosis for elder and newborn babies.
Kinematic-Based Nonlinear Control for an Omni-Directional Robot

In this study, we propose a new control strategy for a robot that utilizes a three Omni wheels for moving. With the construction of the kinematic characteristic equations of the system, it is possible to understand and calculate the correlation between the motions of the motors, the wheels, and the robot. Based on this background, a control strategy for the system is proposed, in which the state variables of the robot corresponding to the desired behavior are calculated and converted to the desired angular velocities of the electric motors. By using the traditional integral sliding controller, the motors are precisely controlled simultaneously with the achievement of the expected displacement of the robot. All simulation results are then given to verify the efficiency of the construction algorithm.
Contrasting Evidences Between High Cholesterol Levels and The Risk of Cardiovascular Disease: A Review

Introduction: The cholesterol hypothesis had been kept alive for decades by reviewers who used statistics that excluded the results from unsuccessful trials and ignored numerous contradictory observations. High dietary cholesterol intake had been associated with development of cardiovascular diseases (CVD) and mortality, for over several decades, without a direct link between the CVD and high serum cholesterol level. Hence, people avoided healthy cholesterol-rich diets due to the fear of developing CVD. The relationship between elevated plasma cholesterol and CVD and criteria for appropriate methods for screening patients with elevated cholesterol had remained a source of medical debates. Lack of decrease in overall mortality rates in patients without clinical coronary disease in whom aggressive lowering of cholesterol was achieved might have contributed to the lack of consensus on this most important issue. Methodology: With information derived from search engines, such as Elsevier, Springer, PubMed, Science Direct, Medline, Google Scholar and a library search for articles published in peer-reviewed journals. Results: Several research results showed that association between total serum cholesterol, its components and CVD is weak, absent or inverse implying that consuming healthy high cholesterol diets may not be harmful to health. Conclusion: This review provides evidence contrasting the links between elevated plasma cholesterol and CVD, and demonstrated that elevated cholesterol concentrations, rather, improve quality of life and life expectancy. In addition, most prevalent methods for cholesterol quantification in biological samples and foods, utilizing new technologies, such as Ambient Ionization Mass Spectrometry are summarized, along with other components of cholesterol. Keywords: Cholesterol, CVD, Quantification
‘I heard myself proclam’d:’ the transition in the critical and textual reception of Edgar in King Lear

The publication of Shakespeare's First Folio in 1623 marked the beginning of a period, lasting over three centuries, in which the critical reception of Edgar in King Lear was often unenthusiastic, judging by the opinions of subsequent theatre practitioners and literary critics. There were voices that questioned Edgar's relevance to the plot and considered him a superfluous character. Moreover, Edgar's heteroglossia was increasingly toned down in stage interpretations, removing the complex polyphony of Shakespeare's character by omitting lines given to the character in the Quarto version of the play. Since the mid-twentieth century, however, the critical (and textual) reception of Edgar has changed, as evidenced by the research of textual scholars who seek to preserve the ethical meaning of Edgar in the play. This has resulted in Edgar's text being revisited in stage performances. In addition, recent critics are paying more attention to Edgar's dexterity in the use of language and disguise, which they believe restores the character's role as crucial to the play's plot.
Classification and hemodynamic characteristics of delayed intracerebral hemorrhage following stent-assisted coil embolism in unruptured intracranial aneurysms

Background and objective Stent-assisted coil (SAC) embolization is a commonly used endovascular treatment for unruptured intracranial aneurysms (UIAs) but can be associated with symptomatic delayed intracerebral hemorrhage (DICH). Our study aimed to investigate the hemodynamic risk factors contributing to DICH following SAC embolization and to establish a classification for DICH predicated on hemodynamic profiles. Methods This retrospective study included patients with UIAs located in the internal carotid artery (ICA) treated with SAC embolization at our institution from January 2021 to January 2022. We focused on eight patients who developed postoperative DICH and matched them with sixteen control patients without DICH. Using computational fluid dynamics, we evaluated the hemodynamic changes in distal arteries [terminal ICA, the anterior cerebral artery (ACA), and middle cerebral artery (MCA)] pre-and post-embolization. We distinguished DICH-related arteries from unrelated ones (ACA or MCA) and compared their hemodynamic alterations. An imbalance index, quantifying the differential in flow velocity changes between ACA and MCA post-embolization, was employed to gauge the flow distribution in distal arteries was used to assess distal arterial flow distribution. Results We identified two types of DICH based on postoperative flow alterations. In type 1, there was a significant lower in the mean velocity increase rate of the DICH-related artery compared to the unrelated artery (−47.25 ± 3.88% vs. 42.85 ± 3.03%; p < 0.001), whereas, in type 2, there was a notable higher (110.58 ± 9.42% vs. 17.60 ± 4.69%; p < 0.001). Both DICH types demonstrated a higher imbalance index than the control group, suggesting an association between altered distal arterial blood flow distribution and DICH occurrence. Conclusion DICH in SAC-treated UIAs can manifest as either a lower (type 1) or higher (type 2) in the rate of velocity in DICH-related arteries. An imbalance in distal arterial blood flow distribution appears to be a significant factor in DICH development.
Chatbot Based on Emotions Using Deep Learning

Human emotions are unpredictable as they change very frequently. Human emotions areinvoluntary mental states of feelings and are accompanied by physiological changes. Emotions which can be detected through the facial expressions play a major role and are very much important for non-verbal communication as they reflect on their faces. In the process of developing computer modeling of human’s emotions, a lot researches have been made. Therefore, a chatbot detecting the emotions can be a better approach for detecting emotions. In this study, it demonstrate that Convolutional Neural Networks (CNNs) offer a superior method for detecting human emotions and develop a deep learning model that can accurately classify the facial expressions. The FER 2013 dataset was used in this study's training. The evaluation of the suggested approach produces pretty accurate findings, which could inspire academics to develop a Chabot model based on emotions in the future.
Dynamic Population-Based Jaya Algorithm for Genetic Biomarker Classification

Cancer is among the leading causes of death worldwide. If we can predict an individual’s risk of developing cancer, then steps can be taken to minimize the risk of developing a malignancy. One of the useful techniques is to identify the genetic biomarkers that are responsible for causing cancer. Detection and classification of these biomarkers require either biopsy computer-aided imaging or microarray data analysis. This paper proposes a new technique for the detection and classification of biomarkers using microarray data analysis. The work is based on a hybrid method where gene selection is carried out using minimum redundancy maximum relevance (mRMR) followed by a dynamic population-based Jaya algorithm. Here logistic map is employed for faster convergence. The majority of the existing evolutionary algorithms (EAs) are based on fixed population size which increases computation time. Therefore, a dynamic population-based evolutionary algorithm is employed here. Specifically, the goal is to investigate if there is a significant advantage of reducing the population size with the increase in iterations. To validate the performance of the proposed method, three state-of-the-art algorithms are selected for comparison over three benchmark datasets. The results of our experiments prove that our proposed method performs better than fixed population-based methods.
Do Different Types of Microphones Affect Listening Effort in Cochlear Implant Recipients? A Pupillometry Study

Background: It is known that subjects with a cochlear implant (CI) need to exert more listening effort to achieve adequate speech recognition compared to normal hearing subjects. One tool for assessing listening effort is pupillometry. The aim of this study is to evaluate the effectiveness of adaptive directional microphones in reducing listening effort for CI recipients. Methods: We evaluated listening in noise and listening effort degree (by pupillometry) in eight bimodal subjects with three types of CI microphones and in three sound configurations. Results: We found a correlation only between sound configurations and listening in noise score (p-value 0.0095). The evaluation of the microphone types shows worse scores in listening in noise with Opti Omni (+3.15 dB SNR) microphone than with Split Dir (+1.89 dB SNR) and Speech Omni (+1.43 dB SNR). No correlation was found between microphones and sound configurations and within the pupillometric data. Conclusions: Different types of microphones have different effects on the listening of CI patients. The difference in the orientation of the sound source is a factor that has an impact on the listening effort results. However, the pupillometry measurements do not significantly correlate with the different microphone types.
A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems

Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.