Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Multifunctional Interlayers on the Performance of Perovskite Solar Cells

The development of perovskite solar cell (PSC) is one of the major advances in photovoltaic technology in recent years due to its lower cost, simple fabrication process, lightweight, sustainability, and being environmentally friendly. However, the power conversion efficiency (PCE) of the conventional PSC is still lower for commercialization. The efficiency of PSC has increased from 5% to 25% within the past few years, where introducing interlayer shows a significant improvement in PCE. In this paper, we numerically investigate the effects of different combinations of interlayers on the performance of PSC using gpvdm simulation software. Three different PCEs of 28.08%, 17.89% and 25.62% are found for three different interlayer combinations i.e., (i) Model A (TIO2/PMMA), (ii) Model B (V2O5/PEDOTPSS) and (iii) Model C (P3HT/ spiroMeOTAD). Both optical and electrical characteristics of different interlayers-based PSCs are investigated by using the properties of different interlayer materials in the simulation. Additionally, the photon distribution, their absorption, and consequent charge carrier generation are investigated while modifying the layer thickness of each unit. This study could be helpful for optimizing the electron and hole transport materials-based interlayers of the PSC.
ABSOLUTE PITCH: SELF-REFERENCE AND HUMAN MEMORY

A number of recent articles have demonstrated the existence of widely held misperceptions and misunderstandings about the nature of "absolute pitch. " Fundamentally, absolute pitch is a cognitive ability that relies on self-referencing (to an internalized pitch template), and a highly developed coding mechanism that links verbal labels with abstract representations of perceptual input. Many researchers in genetics, cybernetics, and other fields (e.g. Baharloo, et al., 1998; Drayna, 1998) labor under the misconception that absolute pitch involves more highly developed perceptual mechanisms, whereas the preponderance of evidence is that absolute pitch ability is an ability of long term memory and linguistic coding (Levitin, 1996). Further, many equate "absolute pitch" with "perfect pitch" whereas in fact, absolute pitch possessors do not perceive pitch any better than non-absolute pitch possessors.
Red Rot Disease Prediction in Sugarcane Using the Deep Learning Approach

The disadvantages of traditional models for disease prediction in sugarcane crops include high expenses for obtaining the data input required to run the model, a lack of geographical data, or a bad dataset. We blend the convolutional neural networks (CNN) model for Red Rot illnesses prediction and classification in our article and also discuss the sugarcane issue. The sugarcane red rot infection is explored in this work. The diseased plant is initially captured on camera via secondary sources. The best characteristics of CNN’s deep learning techniques are used to extract features from the illness and classify it. The study predicts the Red Rot Sugarcane Disease with an accuracy of 93%.
Safety, Reliability and Efficiency – beyond the grid edge

Regardless of how the Grid Edge is perceived, grid users and the society it serves, demand a grid that is safe, reliable and efficient. This goes far beyond the physical ‘grid edge’ and permeates the totality of the distribution system in particular. Earth faults at Medium Voltage have a great impact on safety and reliability. In this paper, the selection of compensated neutral treatment is shown to provide optimized performance. Augmentation with Faulted Phase Earthing or Augmented Residual Current Compensation adds further to the safety performance. This is advantageous in high-risk areas and in wildfire mitigation. Efficiency in returning supply to customers after outages is addressed, including the use of Synchronised Line Monitoring. This paper presents fundamental solutions to deliver safety, reliability and efficiency at and beyond the Grid Edge.
Sample Size Estimation When Time-to-Event is the Primary Endpoint

This paper describes factors influencing sample sizes needed for comparing survival data from two treatment groups. General formulas for sample size calculations are given for comparisons based on the logrank statistic and for comparisons based on the difference in two estimated survival curves at a fixed time point. The importance of various factors is illustrated by numerical examples. Factors that dilute the treatment effect, factors that alter the degree of censoring, and the choice of the test statistic have the greatest impact on required sample size. Adjustments for covariates, adjustments for repeated monitoring of accruing survival data, and adjustments for other types of multiple comparison, however, can also influence required sample size.
Out-of-Band Analysis of GFDM Systems Based on Different Self-Interference Types

This paper studies the impact of out-of-band (OOB) emission and interferences on the generalized frequency division multiplexing (GFDM) systems. Specifically, we analyze the effects of two types of independent self-interference, namely inband and adjacent subcarrier interference, on the OOB performance. We analytically derive mathematical expressions for OOB emission and sufficient conditions for reducing OOB. We investigate the effects of different prototype filters on the OOB performance based on the new formulation of the GFDM system. Our numerical experiments show that the OOB performance is independent of the modulation order and depends on the type of prototype filter and its orthogonality.
Sentiment Analysis on Movie Reviews: A Comparative Analysis

Sentiment analysis is most widely used in NLP to extract and observe opinions of an individual or a group of people based on their own words or on their perspective or views on certain incidents, which could be based on a social media post, a review on a movie or even a feedback for a product purchased online. This technique can directly be used to predict the emotions conveyed by the text like whether it is positive or negative. In this work, a comparative analysis of the impact of various machine learning and ensemble models is presented for Sentiment analysis on movie reviews. Feature extraction methods are also used to reduce the dimensions of the input dataset and to select the most important features which ultimately improved the overall accuracy. Several classification models like Logistic regression, support vector classifier and Random Forest have been implemented along with boosting and stacking ensemble models. The results obtained show that sentimental analysis for movie reviews using ensemble models has higher accuracy when compared to other models. The dataset used for experimentation is the NLTK dataset for sentiment analysis of movie reviews.
Electric Vehicle (EV) Preventive Diagnostic System: Solution for Thermal Management of Battery packs using AIOT

Penetration of E-Mobility recent years in moving towards exponential growth having target towards carbon neutral agreement across the globe. However, electric vehicles come with challenges specific reliability, durability, operability and maintenance. The foremost important is thermal management as directly affects the performance, and operability of the e-mobility. Li-ion batteries used in EV are packed with very higher energy density in the e-mobility triggering to lower thermal stability, leading to safety critical problems, e.g., Thermal Incidents (TR). The operability, service life and cost of battery packs are the driving factors. The battery functional parameters i.e. charge & discharge cycle, peak power during various load profiles depending on the drive terrain are optimized when they are operated at optimized temperature. The variation in temperature has negative impact on battery delivery parameters, overall service life & efficiency of the system. Battery thermal management became critical for safe drivability. Using the abuse parameters of batteries monitored by Battery Thermal Management System (BTMS), the target is to come up with an AIOT based preventive diagnostic system enabling safe drivability and efficient maintainability thereby addressing complete supply chain by recommend/notification predictions to Run-to-Failure (RtF) maintenance, planned Preventive Maintenance (PvM) and Predictive Maintenance (PdM) to end users, dealers, and OEM to manage under development and after sales services effectively. The final target to achieve fault prediction/notification system with AIOT based solution.
Classification of Lung Chest X-Ray Images Using Deep Learning with Efficient Optimizers

This paper investigates the application of several deep learning architectures such as VGG-16, VGG-19, ResNet-50, and Xception Net for lung chest X-ray images, with 5, 10, and 15 epochs, and different optimizers such as Adam, SGD, and RMSProp, and a learning rate of 0.0001. The investigation finds that when adaptive gradient is used, the VCG-16 architecture achieves 68% accuracy; VCG-19 achieves 67% accuracy; ResNet-50 achieves 98.67% accuracy; and the Xception Net architecture achieves less than 50% accuracy. With further experimentation using 5, 10, and 15 epochs and optimizers such as Adam, SGD, and RMSProp, a 100% accuracy was achieved with 15 epochs for the VGG-16, VGG-19, and ResNet-50 architectures. However, Xception Net has been able to achieve only 70% accuracy with these optimizers.
Hybrid Deep Learning Classification Model for Attention-Deficit-Hyperactivity Disorder using functional Magnetic Resonance Imaging

A child with a neurodevelopmental disorder, such as attention deficit hyperactivity disorder (ADHD), faces many challenges in school, like not paying attention when the teacher is taking class, poor performance in academics, and difficulty following classroom behaviour. The functional connectivity of the brain is an essential feature to be investigated to identify such disorders. Developing a computer-aided diagnosis method using a deep learning algorithm to identify ADHD children is the aim of the research. In this study, a hybrid 3D convolutional neural network and bidirectional long short-term memory (3D CNN-BiLSTM) classification model is proposed to classify ADHD and typically developing conditions using resting-state functional magnetic resonance imaging of the brain. The result shows that the accuracy of the proposed hybrid deep learning model gives a maximum accuracy of 75.4% on validation. This indicates that the proposed 3D CNN-BiLSTM classification model for ADHD outperforms the recent existing algorithm by approximately 3% in terms of accuracy using only the functional magnetic resonance imaging dataset obtained from the ADHD-200 repository. Medical professionals may be able to diagnose ADHD in children efficiently by combining the proposed classification model with the present diagnosis based on the child's behaviour.
Design and Implementation of Self-Charging BMS for Electric Vehicle Applications

The design of a self-charging system for electric vehicles (EV) is presented in this work. This concept addresses the drawbacks presented in other EV architectures due to challenges faced during the safe operation of battery packs. However, designing a self-charging system for EVs is a feasible solution to overcome issues related to charging and discharging of the battery pack. To charge the battery pack, a novel single-rotor, double-winding motor design is proposed in this project. The novel motor is designed by concentrating both the generator and motor armature windings on the same rotor. The novel motor design generates the required output voltage at its rotor based on winding technology, which does not require any additional charging circuits for the EVs. However, a bidirectional DC/DC converter with a battery management system is used to operate the battery at safe limits and reliably. Additionally, the solar panel is used in this EV design to charge the battery when the vehicle is in an ideal condition.
Dynamic Community Detection Algorithm based on Allocating and Splitting

Since the incremental community detection method is affected by the initial network community and the incremental detection process, it is prone to generate the problem of error accumulation. To solve the above problem, a dynamic community detection algorithm is proposed. First, considering the closeness between nodes to detect the community structure of the first snapshot network. After that, active nodes that reflect network changes are identified in adjacent snapshots and allocated to communities. Then, during the incremental detection process, considering the influence of adding edges on the accuracy of the community, the edge-added node is defined. In this way, the allocated community structure can be split into multiple local communities and singleton communities constructed by edge-added nodes and other nodes. By merging and optimizing the split communities, the final community structure can be detected. Finally, experiments show that the normalized mutual information and the modularity are improved by an average of 5.23% and 5.00% on synthetic dynamic networks. The modularity is improved by an average of 2.39% on real dynamic networks.
Investigating the Performance of Steepest Descent, Newton-Raphson, and Fletcher-Reeves Approaches in Unconstrained Minimization Problems

Different optimization approaches are explored to solve unconstrained minimization problems numerically in this paper. A classic optimization problem i.e., the Rosenbrock function is considered a benchmark to compare the performance of multiple algorithms including steepest descent, Newton-Raphson, and Fletcher-Reeves conjugate gradient. The performance of four methods including fixed step size, variable step size, quadratic fit, and golden section search region elimination with the steepest descent algorithm is investigated also. The required number of iterations to converge the minimum set point while solving problems is considered an evaluation metric. The converging trajectory of the algorithms is presented using level curves.
A Review On Breaking the Limits of Time Series Forecasting Algorithms

Time Series (TS) forecasting has stagnated owing to algorithm restrictions, therefore systems developed using these methods can only perform so well. TS remains a challenge despite recent advances in Deep Learning (DL) in Natural Language Processing (NLP) and Reinforcement Learning (RL). This paper reviews the literature on these algorithms, highlights studies using them, and shows their limits. Neural Ordinary Differential Equations (NODEs) with continuous-time and continuous-depth tackle TS forecasting issues. Liquid Time-Constant (LTC) networks, a more advanced and reliable implementation of these NODEs, provides fluidity. We propose a new design that uses the LTC's liquid adaptability and is more adaptable to manage immediate changes. These algorithms are more steady, adaptive, and versatile than DL, which may help overcome its TS forecasting shortcomings.
Soft Voting Strategy for Multi-Modal Emotion Recognition Using Deep-learning- Facial Images and EEG

Emotion recognition is an important factor in social communication and has a wide range of applications from retail to healthcare. In psychology, emotion recognition focuses on emotional states within non-verbal visual and auditory cues. It is essential to the human ability to associate meaning with events rather than treating them as mere facts. Studies of emotion recognition often utilize data gathered in response to non-verbal cues using modalities such as eye tracking, Electroencephalo-gram (EEG), and facial video and build classification models capable of differentiating responses to various emotions and cues. The accuracy of these emotion recognition models largely depends on feature representation and the suitability of the chosen features in magnifying the differences between patterns of various emotions. Single-modal feature extraction methods are limited in capturing between-group differences and often result in reduced classification performance. To address this problem, this paper proposes a multi-modal approach in the representation of response to emotional cues involving EEG recording and facial video data. The study utilizes the dataset containing frontal face video recordings and EEG data of 22 participants. A novel deep neural network architecture within the feature level is used to efficiently predict emotions using EEG and facial video data. The experimental result indicates 97.5% accuracy in identifying facial expressions and categorizing them into two classes, arousal (class 0) and valence (class 1), surpassing state-of-the-art for the DEAP dataset.
Real- Time Healthcare Monitoring and Treatment System Based Microcontroller with IoT

Health monitoring systems have achieved great popularity and great importance, especially with the presence of pandemics, large numbers of patients, and a lack of health staff. The presence of sensors on the patient's body to measure blood pressure, body temperature, and heart rate, in addition to room temperature and humidity, constantly supports the specialized medical staff in measuring these indicators. The advantage of these devices is that they are with the patient all the time, and a nurse cannot accompany a patient for this period. In this paper, a health care system is designed and implemented to measure vital signs and room environment temperature and humidity. ESP32 receives the vital signs data from the patient and his room. This information has been sent to the Raspberry Pi 4 where the information was compared to the information provided by the doctor to ensure obtaining the alarm when the measure has a large difference. The system obtains two types of alarms; the first is a medical alarm that accrues when vital signs are high or low from the normal measurements. This alarm calls the medical staff, while the second alarm occurs during a hardware malfunction. The second type of alarm call the technical staff. Testing the system shows that the two types of alarm have been recognized on their occurrence. All these measurements and alarms have been stored in the cloud for patient health monitoring.
Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures

Early and accurate detection of brain tumors is very important to save the patient's life. Brain tumors are generally diagnosed manually by a radiologist by analyzing the patient”s brain MRI scans which is a time-consuming process. This led to our study of this research area for finding out a solution to automate the diagnosis to increase its speed and accuracy. In this study, we investigate the use of Residual Network deep learning architecture to diagnose and segment brain tumors. We proposed a two-step method involving a tumor detection stage, using ResNet50 architecture, and a tumor area segmentation stage using ResU-Net architecture. We adopt transfer learning on pre-trained models to help get the best performance out of the approach, as well as data augmentation to lessen the effect of data population imbalance and hyperparameter optimization to get the best set of training parameter values. Using a publicly available dataset as a testbed we show that our approach achieves 84.3 % performance outperforming the state-of-the-art using U-Net by 2% using the Dice Coefficient metric.
Methods and architectures based on modular redundancy for fault-tolerant combinational circuits

In this thesis, we mainly take into account the representative technique Triple Module Redundancy (TMR) as the reliability improvement technique. A voter is an necessary element in this kind of fault-tolerant architectures. The importance of reliability in majority voter is due to its application in both conventional fault-tolerant design and novel nanoelectronic systems. The property of a voter is therefore a bottleneck since it directly determines the whole performance of a redundant fault-tolerant digital IP (such as a TMR configuration). Obviously, the efficacy of TMR is to increase the reliability of digital IP. However, TMR sometimes could result in worse reliability than a simplex function module could. A better understanding of functional and signal reliability characteristics of a 3-input majority voter (majority voting in TMR) is studied. We analyze them by utilizing signal probability and boolean difference. It is well known that the acquisition of output signal probabilities is much easier compared with the obtention of output reliability. The results derived in this thesis proclaim the signal probability requirements for inputs of majority voter, and thereby reveal the conditions that TMR technique requires. This study shows the critical importance of error characteristics of majority voter, as used in fault-tolerant designs. As the flawlessness of majority voter in TMR is not true, we also proposed a fault-tolerant and simple 2-level majority voter structure for TMR. This alternative architecture for majority voter is useful in TMR schemes. The proposed solution is robust to single fault and exceeds those previous ones in terms of reliability.
Drowsiness and Lethargy Detection Using Machine Learning Techniques

Drowsiness has severe effects on the safety of human life. The worldwide death rate due to drowsy driving is quite alarming. As the implementation of artificial intelligence (AI) is growing faster, this paper describes an attempt to implement machine learning (ML) to detect drowsiness. 120 videos of 60 participants are collected from the Real-Life Drowsiness Video Dataset made by a research team of the Vision-Learning-Mining Lab from the University of Texas at Arlington. Then Eye Aspect Ratio, Mouth Aspect Ratio, Pupil Circularity, and Mouth Aspect Ratio Over Eye Aspect Ratio, Nose Length, Chin Length, Nose Length Over Chin Length Ratio are extracted as features of each participant using the 3D Face-Mesh 468 facial landmarks system from those videos. After that, each feature is normalized by its mean and standard deviation. Then the CSV dataset is generated using seven initial and seven normalized features. A total of 30000 instances are there in the dataset. A total of eight classification algorithms are implemented to build the model. The dataset is split such that the individual in the train set will not be in the test set to test the proposed model's ability to predict drowsiness for new faces. 5-fold cross-validation is implemented to measure performance for each algorithm. Convolutional Neural Network (CNN) yields maximum accuracy (91.63%). The state of any individual's eye closing, rapid eye blinking, yawning, putting a hand on the mouth during yawning, and head posing too much up or down can be detected as drowsiness by the proposed model.
An Effective Emotion Recognition Method Using Facial and Speech Features

In face-to-face conversations, facial expressions help humans communicate nonverbally. Since the early 1990s, academics' focus on automatic facial expression detection, which is essential for human-machine interfaces, has grown. Traditional machine learning techniques can demand a difficult feature extraction procedure and yield subpar results. Using current developments in deep learning, we present efficient deep Convolutional Neural Networks (CNNs) that can automatically and accurately evaluate the semantic information contained in faces and speech without manually defining feature descriptors. Additionally, in order to develop an effective CNNs based emotion recognition system with a high degree of classification capability, we employ various loss functions and training techniques. The experimental results demonstrate that, the proposed method outperforms existing solutions in terms of performance and makes it ideal for real-time systems.