Miracle-dz/UniFAR-FTUs huggingface dataset:
Accident Detection using DenseNet

According to a data, around 1.5 lakh persons die due to road accidents per year in India alone. 30-40 percent of these road accidents go unnoticed or neglected by the general public to avoid the unwanted police inquiry that can cost lives and time of several people. A simple idea can ease the process of controlling the traffic system and detecting accidents. The main goal of this work is to use computer vision and also deep learning to detect accidents through surveillance and dashboard cameras and then report it to nearby emergency services with valid accident images. So suppose we have L number of layers in a typical Dense Net structure there will be about (L*(L+1))/2 layers, so when n number of images are added it is simpler to process, because of the extended layers. Every layer adds only a limited number of parameters. This increases the flow of gradient through the network. Through this the task of DenseNet is accomplished.
Predicting the Stock Price Using Natural Language Processing and Random Forest Regressor

Together with data mining, artificial intelligence and machine learning techniques have been used to rectify a multitude of real-world problems. Such methods have found to be completely successful, resulting in full accuracy with reduced monetary expenditure and preserving massive amounts of time, too. Sentiment analysis is frequently implemented to customer voice components like evaluations and review reactions, web and digital media, and health system components for applications ranging from marketing to customer support to clinical research. Social media is a framework commonly used by individuals to share their opinions and demonstrate sentiments on various occasions. Stock market index forecasting is a tedious task; this is purely since stock data series starts behaving as a similar to arbitrary-walk. The businesses have to hire investment specialists who would take excessively high profits in order to advise on investment choices. Such investment professionals offer an easy approach, which can be used by anyone with an internet connection and a computer. The main objective is to build a reliable, inexpensive and sustainable framework for forecasting the stock market value by implementing sentiment classification to twitter data. The real time twitter data is pre-processed to remove unwanted data and tokenization is applied. The sentiment analysis is applied followed by Random Forest classifier and the graph plots are obtained. X-axis in the resultant graph represents time series and Y-axis represents the closing price.
Analysis of Deep Ensemble Transformer Model for Fake News Detection

The internet and social media have become increasingly popular than ever before with the technology advancements, resulting in a wide and rapid spread of fake news that require urgent actions. Fake news are major threat to the integrity of various organisations, as it weakens public trust in the media, leaving them perplexed. In this paper, an ensemble model (Ensemble R-X-L) is proposed for detection of fake news which comprises of pre-trained XLM-RoBERTa (Robustly optimized BERT Pretraining Approach), ALBERT (A Lite BERT), and XLNet. The performance of the ensemble model is compared with the individual transformer models and several baseline models like LSTM (Long Short-Term Memory) and GRU (Gated recurrent units). The proposed Ensemble R-X-L achieved an accuracy of 0.9841 on a fake news detection dataset from Kaggle. Other performance measures such as precision, recall, F1, and accuracy are also computed, confirming the proposed model’s potential for fake news identification.
Depression Prediction of Human Using Advanced Technology and Medical Imaging

Depression is a typical condition that impacts 3.8 % in total population, with 5.0 % of adults and 5.7 % of oldaged persons suffering from it. Depression can be dangerous to one's health, especially if it is persistent and has a moderate or severe intensity. Despite the fact that there are effective treatments for mental diseases, as well 75% of developing countries do not obtain them. In this paper, a smart gadget (a mobile phone or a robot) and medical imaging are used to identify depression without the need for clinical advice. By comparing the various elements of input and evaluating their performance, efficient technology can be used to forecast depression and ensure privacy.
Design Considerations of A Novel Distributed Key-Value Store for New Storage

The emergence of new storage like persistent memory (PM) and zoned namespaces SSDs (ZNS-SSDs) introduces new challenges and opportunities for distributed key-value stores. Since LSM-tree has been widely adopted in distributed key-value stores, such as RocksDB and HBase, it is necessary to revisit the LSM-tree to make it adapt to new storage. In this paper, we first analyze the challenges of adapting the LSM-tree for new storage. Then, we propose a high-level architecture for a new-storage-aware LSM-tree-based key-value store called Hybrid-LSM. We explain the key structural issues of different storage layers in Hybrid-LSM and present some preliminary design ideas.
Estimation of Equivalent Thermal Conductivity of PCB Airgap Windings

This paper presents a novel lumped-parameter model of the equivalent thermal conductivity of PCB windings referred to as the improved parallel circuit model (IPCM). This model can easily be used during the design phase of the machine thanks to its low computational complexity. When compared to other existing analytical models, with as reference a finite-element model, it is shown IPCM has the highest accuracy, i.e. a relative error of less than 5% on the whole range of typical parameters of PCB windings. An experimental characterization is also performed such as to validate the quality of the model against a custom-made PCB winding.
Approximate Bayesian Inference via Sparse grid Quadrature Evaluation for Hierarchical Models

We combine conditioning techniques with sparse grid quadrature rules to develop a computationally efficient method to approximate marginal, but not necessarily univariate, posterior quantities, yielding approximate Bayesian inference via Sparse grid Quadrature Evaluation (BISQuE) for hierarchical models. BISQuE reformulates posterior quantities as weighted integrals of conditional quantities, such as densities and expectations. Sparse grid quadrature rules allow computationally efficient approximation of high dimensional integrals, which appear in hierarchical models with many hyperparameters. BISQuE reduces computational effort relative to standard, Markov chain Monte Carlo methods by at least two orders of magnitude on several applied and illustrative models. We also briefly discuss using BISQuE to apply Integrated Nested Laplace Approximations (INLA) to models with more hyperparameters than is currently practical.
Diabetic Retinopathy Detection Using Retinal Fundus Picture and Image Enhancement Using Fuzzy Clustering

Patients with diabetes are more susceptible to developing diabetic retinopathy. Blindness can be caused by a disease and can be saved if early diabetic retinopathy detection is made. Retinal exudates and haemorrhages associated with diabetic retinopathy can be easily detected in fundus images due to their sensitivity to vascular disorders. Exudates and haemorrhages in retinal fundus images can now be easily and affordably detected, and diabetic retinopathy can be appropriately classified. The fundus image is clustered into different areas of interest using a k-means colour compression technique, which reduces the colour dimension. The region properties attributes were used to separate and categories the various components of the diabetic fundus. Finally, experiment and trial were used to identify diabetic retinopathy using the knowledge-based fuzzy inference system (FIS). FIS recognize diabetic retinopathy by utilizing the above-mentioned effective attributes and knowledge-based fuzzy inference system (FIS). A deep learning-based Efficient Net-based feature extractor is used to produce feature vectors from pre-processed photos. The MESSIDOR dataset is used for all trials, and the outcomes are evaluated using a range of criteria. The K-Means Clustering with Fuzzy Technique approach outperformed the competition in rapports of diagnostic accurateness.
The realization of higher-dimensional breaking mechanism

We study D-branes on Calabi-Yau threefolds, which are realized through the blowing up the singularity of orbifold. This D-branes are represented as sheaves, which can be stable or unstable, what is connected with the transition in the Teichm$\ddot{u}$ller space. Using the derived category of McKay quiver representations, which describe D-branes as quivers and open superstrings between them by Ext groups, we can represent Higgs multiplets by the moduli space of an open superstring, connecting two McKay quivers. Through the equivalence between the derived category of coherent sheaves and triangulated category of distinguished triangles over the abelian category of McKay quivers we can associate D-branes with quivers or with sheaves, defined on Calabi-Yau. After the dimensional reduction of the ten-dimensional space-time we can receive matter content of the four-dimensional space-time. Thus, a higher-dimensional breaking mechanism is associated with four-dimensional GUT Higgs multiplets and symmetry breaking higgs mechanism.
Predicting Modalities of Dyslexic Students using Neuro-Linguistic Programming to Enhance Learning Method

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student’s perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier.
Simplified Analytical Calculation of PM Machines Magnetic Flux Leakage Factor

The permanent magnets used for generation of magnetic field in permanent magnet machines are placed in more or less magnetically conducting environment – electrical steel and air. This causes leakage of the magnetic flux from its desired path through the air gap. This paper describes simplified analytical calculation of magnetic properties of five most commonly used placements of permanent magnets and compares obtained leakage factors. A verification of the results via finite element method is also included.
Solving geometry conflicts in GA Optimizations with large numbers of geometric parameters

Genetic Algorithm (GA) based optimization with finite element analysis is expected to become a solution for the high performance motor designs which need to satisfy many strict requirements. However, as its use goes towards complex design which having many geometric design parameters, the technique becomes difficult to use. The problem is the fact that the many design parameters lead to geometry conflicts and make the optimization malfunction. The main cause of this is that the geometry conflicts prevent generating an appropriate population at the initial stage of the GA optimization. To overcome this, a technique which consists of an optimization of parameter ranges and a predicted model using machine learning was developed. The proposed technique was applied to an optimization problem of a PMSM with over 30 geometric design parameters confirming that the initial population was appropriately generated and the optimization can be performed successfully.
A Lightweight Deep Learning Model for Agricultural Pathology

A technique to determine diseases in agricultural crops from leaf images using deep convolutional neural networks is proposed. 12 different diseases in potato, tomato and bell pepper crops are determined from leaf images. As many methods proposed in the existing literature are computationally expensive and are restricted to specific plant species, the proposed technique is capable of determining diseases in multiple crops using a single neural network based model while also minimizing computational complexity. The method obtains an F1 score, accuracy, precision, of 93.66%, 93.72%, 93.57% respectively. Enhancements in the performance are seen when compared to the already existing methods.
Analytical Exploration of Harmonics Behavior in Multiphase Machines

This paper explains the reason for harmonics elimination in asymmetrical six-phase machines. The benefits of a 30° shift between two sets of three-phase windings are explored in great detail. A magnetomotive force has been investigated in a manner of harmonics behavior. A transparent explanation has been derived mathematically and interpreted utilizing time-dependent and position-dependent analytical simulations. The paper presents an analysis of the impact of individual harmonic components separately. The procedure shows the exact points where the harmonic components are canceled and clearly states the reasons for their elimination. Six-phase machine with various electrical and mechanical shifts between two sets of three-phase systems has been studied and compared to three-phase machines. The influence of current harmonics on a torque ripple has been examined experimentally on a six-phase permanent magnet synchronous machine.
Surface Electromyography (SEMG) Based Robotic Assistive Device

An EMG-based exoskeleton robot system that assist the rehabilitation process of upper limb has been proposed in this project. The exoskeleton has four degree of freedom (DOF) capable of various motion such as shoulder flexion/extension, abduction/adduction and internal/external rotation, and elbow flexion/extension. This project report describes the hardware design of the exoskeleton robot and the control method after analysing the work of other researchers. The skin surface electromyography (SEMG) signals of muscles and the joint force/torque of the user are used as the input information for the proposed controller. The robot’s motion can be controlled by both the SEMG signals and force/torque signals. Support vector machine (SVM) has been implemented to estimate user’s intended motion based on the SEMG signals. The proposed control system has only been tested in computer simulation. Thus, further testing and analysis are required before the robot prototype can be built.
TOWARDS AN OPTIMAL STRATEGY FOR WLAN LOCATION DETERMINATION SYSTEMS

1 This paper presents a general analysis for the performance of WLAN location determination systems. In particular, we present an analytical method for calculating the average distance error and probability of error of WLAN location determination systems. These expressions are obtained with no assumptions regarding the distribution of signal strength or the probability of the user being at a specific location, which is usually taken to be a uniform distribution over all the possible locations in current WLAN location determination systems. We use these expressions to find the optimal strategy to estimate the user location and to prove formally that probabilistic techniques give more accuracy than deterministic techniques, which has been taken for granted without proof for a long time. The analytical results are validated through simulation experiments and we present the results of testing actual WLAN location determination systems in an experimental testbed. We also study the effect of the assumption that the user position follows a uniform distribution over the set of possible locations on the accuracy of WLAN location determination systems. Knowing the probability distribution of the user position can reduce 1An earlier version of this paper appeared in the 2004 Communication Networks and Distributed Systems Modelling and Simulation Conference
Rigid Holography and Six-Dimensional N=(2,0) Theories on AdS_5 times S^1

Field theories on anti-de Sitter (AdS) space can be studied by realizing them as low-energy limits of AdS vacua of string/M theory. In an appropriate limit, the field theories decouple from the rest of string/M theory. Since these vacua are dual to conformal field theories (CFTs), this relates some of the observables of these field theories on AdS to a subsector of the dual CFTs. We exemplify this `rigid holography' by studying in detail the 6d N=(2,0) A_{K-1} superconformal field theory (SCFT) on AdS_5xS^1, with equal radii for AdS_5 and for S^1. We choose specific boundary conditions preserving sixteen supercharges that arise when this theory is embedded into Type IIB string theory on AdS_5xS^5/Z_K. On R^{4,1}xS^1, this 6d theory has a 5(K-1)-dimensional moduli space, with unbroken 5d SU(K) gauge symmetry at (and only at) the origin. On AdS_5xS^1, the theory has a 2(K-1)-dimensional `moduli space' of supersymmetric configurations. We argue that in this case the SU(K) gauge symmetry is unbroken everywhere in the `moduli space' and that this 5d gauge theory is coupled to a 4d theory on the boundary of AdS_5 whose coupling constants depend on the `moduli'. This involves non-standard boundary conditions for the gauge fields on AdS_5. Near the origin of the `moduli space', the theory on the boundary contains a weakly coupled 4d N=2 supersymmetric SU(K) gauge theory. We show that this implies large corrections to the metric on the `moduli space'. The embedding in string theory implies that the 6d N=(2,0) theory on AdS_5xS^1 with sources on the boundary is a subsector of the large N limit of various 4d N=2 quiver SCFTs that remains non-trivial in the large N limit. The same subsector appears universally in many different 4d N=2 SCFTs. We also discuss a decoupling limit that leads to N=(2,0) `little string theories' on AdS_5xS^1.
Design of Synchronous Reluctance Motor With Minimised Torque Ripple Based on Analysis of Flux Density Harmonics

The paper considers a novel simple approach for the design of SynRM with minimized torque ripple with an example of a 12 kW 24000 rpm Synchronous Reluctance motor equipped with an Axially Laminated Anisotropic rotor (ALASynRM). The method is, especially, handy for ALASynRM. The approach is based on the analysis of harmonics of the air gap flux density distribution and adjustment of the stator or rotor geometry to avoid interaction of stator- and rotor-produced harmonics with the same order.
Study of the Use of an Axial Flux Permanent Magnet Motor for Electric Coolant Pumps

Electric coolant pumps are an essential part of the cooling circuit of electric cars. The design and control of these systems have a major influence on the proper thermal management of the batteries cells. One of the most challenging issue in the development of these devices, concerns the combination of high efficiency and very large pump lifetime. Indeed, the high pressure low flow hydraulic working point imposed by electric vehicles creates an axial force on the impeller which generates friction torque and losses on the bearings.This paper deals with the study of an axial flux motor used for electric coolant pumps. The combination of axial magnetic and hydraulic forces allows the impeller to attain levitation, therefore reducing the friction losses and increasing the lifetime and the efficiency of the pump. Firstly, the motor is modelled. Secondly the hydraulic model is detailed and finally the motor efficiency is reviewed.
Analysis of pneumonia detection systems using deep learning-based approach

Deep learning technology has been utilized to analyze images in numerous medical sectors in recent years. They are very helpful in extracting complicated information from input images. In the classification and detection of lung illness, deep learning plays a critical role. In this article, deep learning methods are described for detecting pneumonia in medical images. The goal of this paper is to explain about the deep learning approaches used in pneumonia detection systems, as well as to discuss recent advancements in the field, as well as to highlight problems and potential future guidelines. A detailed description of deep learning technologies used to treat pneumonia is presented. We will conclude with a discussion of how deep learning methods can be applicable to medical imaging, along with future challenges.
Scube: Efficient Summarization for Skewed Graph Streams

Graph stream, which represents an evolving graph updating as an infinite edge stream, is a special emerging graph data model widely adopted in big data analysis applications. Entirely storing the continuously produced and tremendously large-scale datasets is impractical. Therefore, graph stream summarization structures which support approximate graph stream storage and management attract much recent attention. Existing designs commonly leverage a compressive matrix and use hash-based schemes to map each edge to a bucket of the matrix. Accordingly, they store the edges associated with the same node in the same row or column of the matrix. We show that existing designs suffer from unacceptable query latency and precision in the presence of node degree skewness in graph streams.We argue that the key to efficient graph stream summarization is to identify the high-degree nodes and leverage a differentiated strategy for the associated edges. However, it is not trivial to estimate the degree of a node in real-time graph streams due to the rigorous requirements of space and time efficiency. Moreover, the existence of duplicate edges makes high-degree nodes identification difficult. To solve the problem, we propose Scube, an efficient summarization structure for skewed graph streams. Two factors contribute to the efficiency of Scube. First, Scube proposes a space and computation efficient probabilistic counting scheme to identify high-degree nodes in a graph stream. Second, Scube differentiates the storage strategy for the edges associated with high-degree nodes by dynamically allocating multiple rows or columns. We conduct comprehensive experiments to evaluate the performance of Scube on large-scale real-world datasets. The results show that Scube significantly reduces the query latency over a graph stream by 48%-99%, as well as achieving acceptable query accuracy compared to the state-of-the-art designs.
Supercurrent: Vector hair for an AdS black hole

Hartnoll, Herzog, and Horowitz [Phys. Rev. Lett. 101, 031601 (2008). ] discuss a holographic black hole solution which exhibits a superconductorlike transition. In the superconducting phase the black holes show infinite DC conductivity. This gives rise to the possibility of deforming the solutions by turning on a time independent current (supercurrent), without any electric field. This type of deformation does not exist for normal (nonsuperconducting) black holes, due to the no-hair theorems. In this paper we have studied such a supercurrent solution and the associated phase diagram. Interestingly, we have found a 'special point' (critical point) in the phase diagram where the second order superconducting phase transition becomes first order. Supercurrent in superconducting materials is a well studied phenomenon in condensed matter systems. We have found some qualitative agreement with known results.