Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Assessment on Stock Market Forecast using Long Short Term Memory (LSTM) Algorithm

The stock market price has increased significantly in the current period, attracting firm shareholders. Shareholders and investors both express a keen interest in stock market analysis and forecasting, which finally leads investors and other speculators to contribute to the company's financial success. A favorable prognosis could bring about significant advantages. In today's world, more improved models, various perspectives, and trend analysis tools are developed over time. Nevertheless, the most efficient analytical framework is Long Short-Term Memory, one of the algorithms of Recurrent Neural Networks. This algorithm can be used to produce precise results when the right parameters are used. To accomplish this, a dataset of stock market data must be compiled, and all stock closing prices must be measured using a variety of hidden layers and units. To improve accuracy, proposed work uses SGD optimizer and hyperparameter tuning. The algorithm is evaluated using root mean squared error. As a result of this methodology, historical datasets can be used to forecast the stock market more accurately.
SM2PNet: A Deep Learning Approach Towards Indian Stock Market Movement Prediction

The objective of this study is to forecast the future price movements of equities listed on the National Stock Exchange (NSE) of India. To anticipate the future price movement, all prior statistical and machine learning techniques have focused on identifying latent temporal dependencies. In actuality, stock prices reflect all available information and are influenced by several external variables, making forecasting difficult. One such major element is the interdependence that exists between stock pairings from the same industry. By incorporating this relational dependence combined with the temporal dependency, we may attain improved prediction performance. In order to demonstrate this, we have created a model, SM2PNet (Stock Market Movement Prediction Network), which takes into consideration the characteristics of highly correlated firms in order to examine their impact on the stock price of the selected target company. We’ve analysed predefined sector-industry linkages in order to identify the most relevant peer firms. Sequential model long short-term memory (LSTM) is used to represent temporal dependence. The investigation confirms that SM2PNet beats all other available deep learning methods.
Numerical Experiments with the Adjoint of a Nonhydrostatic Mesoscale Model

Abstract For the purpose of assimilation of radar data into a nonhydrostatic mesoscale forecast model, the adjoint method is considered. For the case of dry convection, a set of identical twin experiments shows that it is possible to construct the initial conditions for the temperature only moderately well from observations of one velocity component only. This behavior can be explained with an ill-posedness of the problem in absence of temperature information caused by the discrete representation of gradients. Small pieces of additional thermodynamic information improve the results noticeably. Contamination of the data with random error has little impact on the quality of the retrieved initial state, as long as there are enough data available. Phenomena not resolved by the data are not retrievable by the algorithm. For the case considered, the adjoint method proves to be a robust tool for data assimilation purposes, but it also leaves the question of how to avoid the ill-posedness open.
Novel, Clock Gating Broadcasting Applications for Low-Power FPGA Architectures

In VLSI technology, clock gating strategies and designs are required for efficient power utilization as well as for other design applications. In this connection, clock is one of the efficient tools for Gating Broadcasting Applications for Low-Power FPGA Architectures. The proposed method mainly explores the use of clock gating strategies to reduce the power utilization for streamed applications that result from asynchronous dataflow architectures. Streaming applications include a wide variety of computer methods from diverse fields as digital logic, digital content coding, encryption, etc. The dynamically streaming nature of algorithms is taken into account in this research to present a set of strategies that can produce power usage by selectively shutting off sections of the circuitry when they are briefly dormant. These methods may be included into the synthesis phase of a high-level dataflow design flow without regard to the semantics of the program being designed. At scale implementations based on field-programmable gate array platforms show that power may be reduced without impacting bandwidth utilization, according to experimental studies.
A Quasi FSS Reflector with Dualband Cross Dipole for 5G Base Station Antenna Application

This article describes the application of a quasi-frequency selective surface (FSS) reflector to a cross-dipole antenna for a 5G base station communication system. The quasi-FSS is composed of a square patch resonator with a resonance frequency of 1800 MHz and a perfect electric conductor (PEC) that reflects electromagnetic waves of all frequencies. Moreover, it can support and reflect two linear polarizations (±45°) of a dual-band cross-dipole antenna with dual operating frequencies of 700 MHz and 1800 MHz. At 700 MHz and 1800 MHz, the impedance characteristic of the antenna with reflector is approximately 26.83% and 34.86 % for Sll and S22 ≤-10 dB (±45°). The measured peak gain of the antenna is around 5.74 dBi at 775MHz and 11.88 dBi at 1850MHz. With a quasi-FSS reflector, the proposed antenna can also create a directional radiation pattern and facilitate the utilization of a 5G mobile communication base station.
A Programmatical Method for Real-time Simulation of Black-box LSTM-based Models of Power Electronic Converters in Hypersim

Black-box modeling methods are essential for modeling industrial over-the-shelf power electronic converters where datasheets might not be readily available. Modeling methods based on machine learning are data-driven, which is an advantage considering that many signals are already being logged in modern power systems due to the high penetration of smart and distributed generation. Hypersim is a powerful tool for real-time simulation of power systems as well as power electronics. This piece of software can directly import MATLAB Simulink models into its environment. LSTM-based Black-box is a promising machine-learning modeling method. However, Hypersim does not support importing LSTM blocks of MATLAB out of the box. This paper proposes a programmatical method for converting LSTM-based models of power electronic converters into the basic blocks of Simulink so that they can be exported to Hypersim for real-time simulation. The performance of the proposed method is assessed via Simulink and Hypersim simulations.
Choosing Solutions for Current Problems in Operational Logistics Featuring Self-Learning Systems

For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can prioritize solutions to answer disturbances. Several ideas are part of the discussion on how to identify the most suitable or promising solution to a deviation. Furthermore, these different approaches converge to a single holistic approach via a step between. The rules, individual and holistic, also feature a mathematical expression. Furthermore, future possibilities for enhancement complete the paper.
Research on the Impact of Digital Financial Inclusion on Urban-rural Residents’ Consumption Gap Based on an Econometric Model

Using a static panel analysis model, a logistic regression model, and a dynamic panel analysis model, respectively, the related relationship between the digital financial inclusion index and the urban-rural residential consumption gap is investigated based on the provincial panel data of 31 provinces (autonomous regions) in China from 2015 to 2019. It is found by the study that the increase in digital financial inclusion index significantly contributes to bridging of the urban-rural residential consumption gap, despite heteroskedasticity and endogeneity. Meanwhile, the increase of urbanization rate and the positive government intervention also contribute to the reduction of this gap, while the upgrade of industrial structure and the expansion of economic openness will hinder the reduction of this gap but the results are not significant. The findings of the study provide a reasonable path to effectively alleviate the Chinese urban-rural residents’ consumption gap, and lay a theoretical foundation for further solving the real problem of urban-rural unbalanced development.
A Traffic Road Sign Recognition System using Raspberry pi with Open CV Libraries Based On Embedded Technologies

The use of computer systems for the purpose of image processing, which is also known as digital image processing, has applications in a variety of sectors, some of which include geology, oceanography, medicine, and space exploration to name a few. The major objective of this piece is to provide examples of different ways in which image processing techniques may be used even in environments with limited computational resources. In particular, we developed a traffic sign recognition system that is capable of reading and interpreting speed signs. This system is based on embedded technologies, and it was constructed by us. This research covers a wide range of topics, some of which are the characteristics of speed limit signs, the requirements and challenges of constructing an embedded real-time base system, and the methods for manipulating numbers that make use of image processing techniques that are based on form and dimension analysis. The study also provides a presentation of, and a demonstration for, several classification and recognition strategies. This research brings to light a number of difficulties with the consistency of color identification as a consequence of lighting circumstances; as a result, avoiding the usage of color models may result in a more effective solution than using color models. In addition to this, color analysis plays a particularly significant role in a broad range of other applications for the identification of road signs. These applications may be found all over the world. For the most part, lightweight solutions were selected for this project since the real-time applications that could be used as well as the capabilities of the Raspberry Pi were severely limited. The Raspberry Pi is the primary focus of concentration for the implementation as a whole. The Raspberry Pi is able to connect sensors, databases, and the results of image processing, in addition to carrying out actions on devices that are external to itself.
Effects of buoyancy on the lower branch modes on a Blasius boundary layer

The effect of thermal buoyancy on the stability properties of lower branch Tollmein–Schlichting waves are investigated. At moderate values of thermal buoyancy the standard triple deck structure, which describes the evolution of such short wavelength instabilities in a buoyant boundary layer, is unaltered. The leading order eigenrelation is now a function of thermal buoyancy and from it we can derive the new dominant length-and time–scales for the instability in the case when the boundary layer is strongly buoyant. These new scales demonstrate that, in the case of strong wall cooling the lower branch structure is identical to the upper branch structure, thus suggesting that the curve of neutral stability may become closed at some large value of a Reynolds number. In the alternate limit of strong wall heating the evolution of a fixed frequency disturbance is governed by the linearized interactive boundary-layer equations; in this case wave–like disturbances cannot be described by any form of the quasi–parallel approximation theory.
Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Buildings

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings’ state of conservation. For that reason, infrared thermography has become a powerful tool to characterize these buildings’ state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this phase of the research project, it was used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in buildings. The model’s accuracy was compared between Multi-Spectral Dynamic (MSX) images, conventional and thermal images, all acquired from two distinct thermal cameras. Fused images (formed through multisource information) were also used, so the influence of the input data and network on the detection results were analyzed.
Detection of Brain Tumors Through the Application of Deep Learning and Machine Learning Models

One of the most deadly conditions that can afflict both children and adults is a brain tumor. Brain tumors progress rapidly, decreasing the patient’s probability of survival if they are not effectively treated. The importance of finding brain cancers early cannot be overstated. For a variety of reasons, including the intricacy of the neuronal structure and size, as well as the form of the brain, diagnosing brain tumors can be challenging. One of the most frequent methods for detecting and localising a brain tumor is magnetic resonance imaging (MRI). Due to the complexity of brain tumors and their qualities, manual inspection might be error-prone. Therefore, early diagnosis of malignancies in the brain necessitates the development of an automated approach. To address this research challenge, this study offers a machine learning method that, given a dataset of MRI images used to classify brain tumors, can generate a labelled segmentation of those images. Four distinct categories of pictures of brain tumors are included in the dataset, including pituitary, meningioma, glioma, and no tumor. In this study, a high-pass filter was employed to demonstrate the diversity of the MRI pictures and how well they integrated with the input data. Slices were combined with a high median filter. The input MRI brain pictures were refined by smoothing them out and highlighting their borders to increase the quality of the output slices. Since the thresholding cluster matched pixels in the input MRI image, the 4-connected seed-growing technique was applied. The model is trained using supervised machine learning classification techniques, such as Support Vector Machine (SVM), K-Nearest Neighbors, Random Forest, Naive Base, and Convolutional Neural Network (CNN). The CNN technique achieved the best results.
Development of Large-Scale Precision Farming Monitoring System based on REST API and SignalR

Farming is one of the primary resources to provide human food therefore, it takes a large amount of cultivated land to meet food needs. Currently, technologies such as IoT have significant role in advancing agriculture in this digitalize era by improving conventional agriculture methods by not only making it optimal but also making it cost efficient for farmer to produce quality of crops. The aims of this paper to develop system in the form of REST API service by using . net 6 as framework to efficiently manage the growth of IoT devices from the ground up in medium scale to large scale cultivated land with easy to scale up based on the SignalR library and crops health monitoring. The system will assist institute farmer and technician by visualizing data from each deployed IoT device (Temperature, humidity, soil moisture, Ph) from each section of cultivated land and management of each new IoT device such as sensor, actuator and microcontroller that will be deployed and by using SignalR library as an interconnection between device to API. To ensure the quality of the system author measured using the JMeter application by conducting simulation with high peak of user access and transmitted data of IoT device to give a validation of load testing that the system will run smoothly without any problem. The research concludes based on the testing that been done has successfully delivered and shown no latency or error with performance result on each test 21ms functional and 0.50ms on SignalR testing.
Soft Computing and the Applications based on Machine Learning for the Healthcare Organization

The soft computing process is an important factor that plays an impactful role in the health care organization. Additionally, the impact of the machine learning process has to be generated within the healthcare system. The approximate application for the health care organization has to be determined and it helps to improve the structure of the health care institution. Solving different kinds of problems have to be evaluated within the healthcare administration. Therefore, different topic-related objectives have to be evaluated in this research article. Moreover, the methods that are used in this article have to be analyzed within this portion. Additionally, detailed information about the soft computing process and its role within the health care administration has to be evaluated. Moreover, the positive impact of the machine learning process has to be determined within the organization.
Diagnosis of Lung Cancer Nodules in CT scan Images using Fuzzy Neural Network

Lung cancer has the loftiest mortality rate of any type of cancer. Detection of lung cancer is one of the most significant medical problems because of its challenging and subjective human interpretation. Image processing tools are generally used in the early stages of opinion and treatment. Timing is critical in relating anomalies in target images, especially in the case of nasty tumors like bone and lung cancer. Beforehand and accurate discovery is crucial for evaluating the likelihood of treatment and can increase the chances of surviving lung cancer. CT reviews are used to describe lung cancer because they give a clear picture of the tumor inside the body and allow for the shadowing of its growth. Pre-processing ways are used in this study, and image quality and delicacy are the primary considerations.
Emotion Prediction through Facial Recognition Using Machine Learning: A Survey

An application created by integrating Flutter and machine learning is one that predicts or manages stress. Its sole and primary function is to identify the stress and pressure that today’s population is experiencing and recommending them ways to deal with it. Humans are constantly activated by a variety of unnoticed environmental stimuli. Sometimes these stressors are undetectable. However, the machines are capable of handling this well, allowing us to create a programme that can make decisions instantly. Today, a lot of scientists and researchers use the information gathered from the internet to help them make the right choices. In order to determine if a person is under stress or not, we are once again using image recognition techniques and camera data in this application. This software takes a picture with the camera, processes it, then decideswhat to do with it and displays it on the screen. There will be a different section in application where multiple methods to deal with stress will besuggested.
Research on Real-Time Collision Detection Based on Hybrid Hierarchical Bounding Volume

Analyzing the performance of various hierarchical bounding volumes(HBVs),a fast algorithm for real-time collision detection(CD)was proposed using a structure of hybrid hierarchical bounding volume(HHBV)based on OBB and sphere BV.The HHBV tree for CD falls into three successive layers in a top-down manner,which are Layer X using sphere as its tree node,Layer Y using OBB-Sphere BV and Layer Z using OBB.The intersection test uses sphere vs.sphere or sphere vs. OBB method for fast overlap test in upper layers,eliminating the possibility of collision between objects far apart;then it accurately determines the contact status among objects in closer proximity by OBB vs.OBB test in lower ones.Experiments were compared with other competitive algorithm(OBB)to analyze the performance.Results show the algorithm is as efficient in precision as OBB,and faster in complex virtual environment.
An Econometric Analysis of Revenue Diversification Among Selected Indian States

The key objective of this article is to empirically examine the trends and determinants of revenue diversification with respect to 14 major Indian states. The findings highlight a gradual decrease in the level of revenue diversification, which has become more visible in recent years. This indicates an erratic pattern of growth in tax and non-tax revenue sources. The panel cross-sectional–autoregressive distributed lag model test results reveal a positive contribution of economic and institutional factors, as compared to political factors, toward the process of revenue diversification. Overall, it is evident that cyclical fluctuations in the major tax revenue sources, coupled with a lessened emphasis on rationalising the structure of non-tax revenue sources, seem to have had an adverse impact on the process of revenue diversification on the part of states. JEL Classifications: H0, H1, H2, H7
Identifying the dataset to define the optimal timing of histopathological examination for central nervous system toxicity in MPTP-induced Parkinson’s disease monkey model

Determining the optimal timing for histopathological examination following exposure to a test article is crucial for assessing neurotoxicity. However, no study has focused on identifying an ideal dataset to define the optimal timing for histopathological examination of central nervous system (CNS) toxicity in monkeys. Therefore, this study aimed to define a predictive endpoint that would guide us in selecting the optimal timing for histopathological examination of CNS toxicity in monkeys. Four cynomolgus monkeys were administered 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) intravenously at a dosage of 0.6 mg/kg twice at 1-week intervals. Necropsies were performed 1 week after the final dose. The Parkinsonian rating (PR) score and temporal changes in neurofilament light chain and glial fibrillary acidic protein concentrations in the cerebrospinal fluid (CSF) and serum were evaluated and compared with the histopathological findings in the brain. The PR score of all animals administered MPTP increased from days 10 to 11, with some degree of individual variability. Microscopically, all animals showed axonal swelling and vacuolation, with or without microgliosis in the nigrostriatal bundle. However, substantial neurodegenerative findings were observed only in animals with high PR scores at necropsy. A slight increase in CSF biomarker levels at necropsy was also observed in animals with high PR scores. However, their correlation with microscopic findings in these animals was unclear. These data suggest that comprehensive clinical observations, such as PR score alone or combined with other CSF biomarkers, could be further evaluated as potential indicators for triggering anatomic CNS evaluations in monkeys following toxic insults.