Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Long Short Term Memory based Stock Price Prediction Techniques

The stock market is a widely-known financial vehicle with a significant influence on the global economy. It will be every investor’s ambition to be able to foresee the market’s rise and fall and profit handsomely from doing so. However, the future of prices is quite uncertain. Even though several types of analysis such as fundamental analysis, technical analysis, etc. have existed for quite some time. Today, several algorithms are utilized to make predictions about prices in the future. The ability to accurately forecast future stock prices is crucial since it provides traders and investors with valuable information about the market’s potential direction. Therefore, they will be able to enter or leave the market at the optimal moment. The most difficult endeavor is to foresee the stock market. There are many aspects to consider, both objective and subjective, rational and illogical. The combination of these elements makes it not only difficult but impossible, to make an accurate prediction of the price of shares. As part of our research, we analyzed several Neural Network techniques, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN), both of which are effective in predicting stock price.
Adaptive automated solution of the multi-group diffusion equations

This paper presents fully automated hp-refinement strategies applied to multigroup diffusion equations. Exponential convergence rates are achieved for a fraction of the number of unknowns needed with uniform refinement and h-refinement strategies. Innovative results include (i) the treatment of adaptivity in the context of the multigroup equations, and, (ii) the definition of a mesh adaptation strategy in the context of eigenvalue outer iterations and thermal iterations. (authors)
Weather Prediction using linear regression model

Weather prediction has been a delicate task in the meteorological department since times due to varied reason similar as the drastically unpredicted behaviour of climate. These predictions are important because they impact diurnal life and also affect the frugality of a state or indeed a nation. Our model uses machine literacy algorithm to predict the weather. Our model is intended to provide accurate weather prediction. Long-term climatic change on Earth and the goods it’ll have on current and unborn generations’ lives are both unknown.
Uniform Resource Clustering for Service Function Chaining in NFV-based Cloud Environment

Network function virtualization (NFV) and software-defined networking (SDN) have had a significant positive influence on the development of technology that mostly resolves the problems with conventional network management systems. Hypervisors enable the virtualization of numerous network functions and services. Virtualized network services such as firewall, load balancer, and network address translator (NAT) along with others can be chained together to form a service chain. A data packet can go through the service chain as per its defined policy and not every available service. Such service function chaining (SFC) policies can be hosted at end devices to conserve a significant amount of energy and time. In this work, we have proposed an approach in which the SFC policies use an adequate amount of network parameters and a technique that uses uniform resource clustering (URC). Two Parameters namely bandwidth and CPU utilization are considered. Our findings indicate that the URC approach saves energy and time significantly. The average network service time with our approach is 9% less than the default approach.
Obsessive UX: Role of UX & Heuristic Evaluation in Building A Product that Attracts Human Emotions

User Experience (UX) is a vast and inclusive field. No product is remained untouched by modern design sensibilities. In a vast spectrum, user experience mainly targets managing a person’s behavior, attitudes, and emotions toward any product. UX specialists work with users' pain points and investigate ways to solve the particular problem and create feasible solutions. The product with which the user is emotionally attached builds significant legitimacy and contributes toward the product’s business growth. Nowadays, it is significant to work for product teams across the globe to work on results and studies that help address common experience & interface design problems that clearly express the natural character of the product. The relationship between psychology and design in development goes hand in hand. The emotional attraction of the user determines a customer’s attraction toward a product. This paper overall focus on how one can build a customer-obsessed product that makes them emotionally attached to it. Further, the paper depicts some important frameworks and heuristic evaluation processes that work toward product success. This paper will serve as a handbook to UX designers across the globe in building the most iconic and memorable brand elements.
An Accurate Estimate of Effective Thermal-Mechanical Properties of Coreless Circuit Substrate for Advanced Packaging

This study aims to give an accurate estimate of the effective thermal-mechanical properties of a coreless circuit substrate for advanced IC packages. To achieve the goal, a subdomain-based effective method is proposed, incorporating a sub-domain three-dimensional (3D) copper trace mapping and modeling (TMM) method, a volume-averaging rule of mixture (ROM) technique and finite element modeling. Furthermore, the proposed effective method is combined with an FEA-based process simulation framework to investigate the process-induced warpage of a flip-chip chip scale package during fabrication. At last, parametric analysis is performed to identify the crucial factors influencing the process-induced warpage of the package.
A UAV and Deep Transfer Learning Based Environmental Monitoring: Application to Native and Invasive Species classification in Southern regions of the USA

Unmanned aerial vehicle (UAV) applications can be powerful tools in horticultural research. However, they have not yet been widely explored in the literature. One significant area of interest for the horticultural community is identifying invasive plant species that, if left unchecked, can hinder the growth and health of native plant species. To address this issue, we assembled a novel data set of invasive and native plant species for seven southern states in the United States and developed a plant classification technique using pre-trained convolution neural networks and transfer learning. We explored extracting features from our data set using several state-of-the-art deep convolution neural network models, including InceptionV 3, MobileNetV 2, ResNetV 2, VGG16, and Xception. We then used the extracted features to classify the plant species using a convolutional neural network with cross-validation. Our experiments demonstrated the potential of our proposed method for achieving performance with a 94% accuracy using the MobileNetV2-DCNN model with data augmentation, hyper-parameter optimization, and the softmax classification technique. The advantage of our approach is the learning process is automated and highly accurate. The data set to train the plant species classifier will be available on request.
Design of a Digital PR Controller for Harmonic Compensation of Single-Phase Grid-Tied Inverter with System Parameter Uncertainty

A key part of any DC to AC conversion device is the high frequency filter with the primary objective to eliminate high frequency switching harmonics introduced by the converter switches. So, the design of a properly tuned filter is of utmost importance to ensure proper voltage and current injection into the grid. If the values of the filter components change over time due to environmental factors, the inverter output will degrade. The paper proposes a single-phase grid tied inverter using PR controller in combination with Harmonic Compensator (HC) to eliminate higher order harmonics in the inverter output caused by improper filter design or the filter becoming out of tune over time. The performance of the inverter is examined with and without the HC to determine the effectiveness of the proposed system. The design of the PR controller and the HC is done using MATLAB and testing was performed on a 2 kW IGBT based grid-connected single-phase inverter using dSPACE 1104.
Prediction of Human β-secretase 1 (BACE-1) Inhibitors for Alzheimer Therapeutic Agent by Using Fingerprint-based Neural Network Optimized by Bat Algorithm

Dementia is a fast-growing public health problem, with an estimated 47 million people currently living with the condition. By 2030, this total is predicted to reach 75 million. By 2050, it will have tripled were, given the urgent need to address this problem. Alzheimer's disease is characterized by a steady decline in cognitive capacities beginning with a decrease in the brain's capacity to form new memories. Significant attention has been focused on developing therapeutic strategies and drugs to treat Alzheimer's disease, which is the most common form of dementia. In this study, the feature used is the PubChem Fingerprint representing the molecule's structure with a total of 822 data for class 0 and 691 data for class 1. We developed a fingerprint-based artificial neural network (ANN) model to predict Beta-secretase 1 (BACE-1) inhibitors as therapeutic agents for Alzheimer's disease. Three optimization strategies, namely the Bat Algorithm, the Hybrid Bat Algorithm, and the Adaptive Bat Algorithm, were used to optimize the architecture of the ANN. This nature-inspired optimization technique mimics the echolocation behavior of bats. The best model was obtained from ANN optimized using Hybrid Bat Algorithm with the value of accuracy and F1-score are 0.81 and 0.78, respectively.
Neural Machine Translation Approach for Low-resource Languages using Long Short-term Memory Model

Communication is a form of translation that human learns naturally since early childhood. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. One example of high-impact language translation is translating health protocols from the World Health Organization to many national languages to prevent the spread of the Covid-19 virus. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of the Long Short-term Memory model as a machine translation involving Bahasa Indonesia and the Sundanese languages. Performance of the Sundanese language to Indonesian language translation is 0.93 average training and validation accuracy and 0.91 average testings BLEU score. Whilst, Indonesian language to Sundanese language translation is 0.92 average training and validation accuracy and 0.88 average testings BLEU score. Both models achieve 0.39 average training loss and 0.38 average validation loss.
ROS-Based Digital Twin for Power Wheelchair

It is notorious these days that there are several limitations for wheelchair users, namely the accessibility in public spaces, the financial capacity to acquire a model that is suitable for their needs, and the lack of knowledge regarding the wheelchair's components. With millions of users around the world, and a high percentage of them reporting at least one accident in the last three years of use, it becomes necessary to find a viable solution for all of these problems. In order to tackle them, a Digital Twin of a power wheelchair was developed, whose purpose is to monitor and store the state of its components and replicate its movements, using a virtual model. Instead of a real power wheelchair, an identical prototype was created based on the LEGO EV3 module integrated with a CMPS11 compass module. The virtual entity itself was developed in the Robot Operating System (ROS) environment, using the Gazebo simulator for visualization of the 3D model, which was created using Blender, a 3D computer graphics tool. To monitor the wheelchair's tilt variations and component status, a dashboard interface was developed using Node-RED, containing several graphs, alarm features and an option to store the generated data in a CSV file.
A torque-based MRAS estimator for position/speed sensor-less control of DFIG systems

In this paper, an electromagnetic torque-based model reference adaptive system (MRAS) is proposed for sensor-less control of doubly-fed induction generator (DFIG) systems. The proposed electromagnetic torque based MRAS estimator aims at extracting the rotor speed in the synchronous reference frame. The advantage of the proposed MRAS estimator is its simplicity due to the fact that it requires only the three-phase rotor current and the reference three-phase rotor voltage as inputs. The small signal analysis is used to conduct stability study of the proposed rotor speed estimator. The PI gains of the MRAS estimator are determined using pole placement. The performance of the proposed rotor speed estimator is validated under various operating conditions of the DFIG using MATLAB/SIMULINK.
Similarity Analysis of Industrial Alarm Floods Based on Word Embedding and Move-Split-Merge Distance

In industrial facilities, alarm systems are essential for process monitoring. However, due to the simplicity of alarm configuration and the poor performance of the alarm system, alarm floods often happen. Similarity analysis of alarm floods compares alarm flood sequences to look for common patterns. These patterns can offer information that is helpful in identifying root causes of alarm floods. Existing methods for alarm flood similarity analysis can only conduct similarity measures based on match operations for alarms represented by text strings; as a result, the match operations ignore the correlations between alarm occurrences. In this work, a new alarm flood similarity analysis method based on word embedding and Move-Split-Merge (MSM) distance is proposed, in order to reveal sequence similarity from a new perspective. The contributions are mainly twofold: 1) The correlations of alarm occurrences are considered in the alarm encoding via word embedding; 2) The MSM distance is calculated to analyze the similarity of alarm flood sequences of different lengths. Then, clustering results are compared with the original true labels of the alarm floods. The effectiveness of the proposed method is demonstrated by a case study with alarm data generated by a public industrial model of the Vinyl Acetate Monomer process.
Enhanced Cu-to-Cu Bonding by Using Sn Passivation Layer

This study discusses the advantage of Sn-passivated Cu-to-Cu bonding compared to Cu-to-Cu direct bonding. However, Sn-capped Cu bonding in the semiconductor industry has been studied since 15 years ago, and seldom research indicated that the Sn passivate layer can tolerate surface height difference in the Cu-to-Cu bonding. Moreover, in the previous related study, the Sn layer thickness was thicker than 1.2 μm, and the bonding temperature was higher than 250°C. In this experiment, 1 μm Sn is electroplated over the Cu to compensate for the Cu surface height difference and lower the bonding temperature and pressure. Furthermore, the process was done by thermal compressive bonding (TCB), and two temperatures, 250°C and 220°C, were conducted in the experiment. Furthermore, 1 MPa and 1-minute bonding condition is used in the experiment to meet the low-pressure and high-efficient bonding process. To conclude, by passivating Sn over Cu, severe condition is not required.
On the Remote Control of Differential Drive Mobile Robots through Wireless Networks

In the present paper, the problem of independent control of the speed and orientation of a differential drive mobile robot, through wireless networks, is studied. Based upon the linear approximant of the nonlinear model of the mobile robot, a multi-delay linear I/O decoupling controller is designed. The controller feeds back delayed measurements of all state variables of the system. The analytic form of the precompensator matrix and part of the elements of the feedback matrix is determined. The remaining controller parameters are metaheuristically evaluated so that appropriate delay stability margins are accomplished. The performance of the proposed control scheme is illustrated through series of computational experiments, where it is observed that the nonlinear model behaves satisfactorily for large external commands and uncertain model parameters.
Reproducing Fear Conditioning of Rats with Unmanned Ground Vehicles and Neuromorphic Systems

Deep learning accomplishes remarkable success through training with massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationship between concurrent events. This learning paradigm is referred to as associative memory. The successful implementation of associative memory potentially achieves self-learning schemes analogous to animals to resolve the challenges of deep learning. The state-of-the-art implementations of associative memory are limited to small-scale and offline paradigms. Thus, in this work, we implement associative memory learning with an Unmanned Ground Vehicle (UGV) and neuromorphic chips (Intel Loihi) for an online learning scenario. Our system reproduces the classic associative memory in rats. In specific, our system successfully reproduces the fear conditioning with no pretraining procedure and labeled datasets. In our experiments, the UGV serves as a substitute for the rats. Our UGV autonomously memorizes the cause-and-effect of the light stimulus and vibration stimulus, then exhibits a movement response. During associative memory learning, the synaptic weights are updated by Hebbian learning. The Intel Loihi chip is integrated with our online learning system for processing visual signals. Its average power usages for computing logic and memory are 30 mW and 29 mW, respectively.
A Deep Learning Approach Based on CT Images for an Automatic Detection of Polycystic Kidney Disease

Deep Learning is a subdivision of machine learning; deep learning is mainly separated into multiple neural network layers. These layers allow it to learn large amounts of the data and try to mimic the human brain. By adding more hidden layers it can help to improvise the efficiency. Deep Learning techniques are utilized to identify diabetes mellitus, vascular dementia early diagnosis and mammography nodule ultrasonographic recognition. Pathology and Radiology will be invested in the future for new advancement in deep learning. Kidney irregularities is one of the most common diseases among people of all ages. A global shortage of urologists developed a deep learning model to diagnose kidney problems with this study. The kidney area is surrounded by a thick wall. It is possible to have multiple cysts with high moisture concentrations that cause renal diseases. The units from zero -twenty indicate different types of renal diseases. Tissues that can be grouped together form a solid lamp known as tumor. Tumors can cause direct damage to glands, spinal cells and other organs. Urolithiasis is another name for renal stone disease where a large amount of solid materials enter into the digestive tract. In a research paper, CNN is trained to learn how the cysts diagnosis should be done. Tree Convolution neural deep learning techniques VGG19 are used to detect kidney abnormalities. To train and evaluate a dataset of 4000 abdomen computed tomography with classifier and segmentation is created. The increased categorization and accuracy rate obtained to display the efficacy of kidney disorders is 98%.
Decentralized Droop Control Strategies for Parallel-Connected Distributed Generators in an AC Islanded Microgrid: A Review

This paper reviews the different decentralized droop control strategies in an AC microgrid. Microgrids are made up of parallel-connected distributed generators (DGs), storage devices and loads operating with coordinated control. They often integrate Renewable Energy Sources (RES) as DGs. The grid can operate in grid-connected and autonomous modes. Due to the intermittent nature of RES, the imbalance between power demand and power supply, the presence of different types of loads and line impedance mismatch, effective sharing of active and reactive power, as well as the regulation of frequency and voltage become imperative in the islanded mode of operation in order to ensure system stability. Droop control is preferred over other control strategies for sharing active and reactive power because it does not require communication channels. This paper review presents the merits and demerits of decentralized droop control methods. Furthermore, possible suggestions for improvement on active and reactive power sharing for parallel-connected DGs are presented including voltage and frequency regulation in an AC islanded microgrid.
Performance Evaluation and Energy Management System for Parallel Hybrid Electric Vehicle

Today, worldwide, there is large research work going on to develop hybrid electric vehicles (HEVs) which relieve the nation from the dependence on fossil fuels like petrol, diesel and vehicles which do not pollute the atmosphere. To achieve this objective, HEVs using alternate fuel sources like fuel cells, batteries, capacitors, flywheels are used with an electric drive system. In some HEVs there are more than one drive system, like an electric drive system along with a conventional engine (ICE) based drive system. These drive systems are used in various combinations like series/parallel depending upon the design adopted. In this paper, it is proposed to evaluate the performance of existing and HEVs under development. Based on the study of the HEVs and their performance evaluation, it is proposed to develop an energy system with one or more power sources and an energy management and control sources (EMCS). The EMCS will effectively manage these power sources. These power sources will be tailor made to match a suitable drive system for an HEV. The drive system will be appropriately selected based on the above study. Simulation work is carried out using state-of-the-art software tools like MATLAB - SIMULINK. The simulation results will be validated by comparing with an appropriate baseline specification of HEV.