Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Building a System for Predicting Diabetes and related conditions using Machine Learning

The objective of this paper is to develop a system for predicting diabetes and related conditions in patients using Machine Learning techniques with high degree of accuracy so patients can be treated at an early stage, which could provide a life-saving impact. A Backpropagation Neural Network (BPNN) with 50 nodes in hidden layer and K-Nearest Neighbour (KNN) were created to predict diabetes in patients. A Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) with 100 nodes in hidden layer were created to predict blood glucose levels and generate early warning signs for short-term diabetes complications such as hypoglycaemia, hyperglycaemia and pre-diabetic. The BPNN model achieved the best performance for predicting diabetes with an average classification accuracy of 76.7% and was compared with KNN model which achieved an average classification accuracy of 74.0%. While LSTM model achieved the best performance for predicting blood glucose levels with an average classification accuracy of 90.0%, 88.8% sensitivity, 88.0% specificity, 93.0% positive predictive value and 81.3% negative predictive value, and was compared with RNN model which achieved an average classification accuracy of 84.1%. Obtaining highly accurate predictions on future readings shows potential for the system to be used by healthcare care personnel to determine the right form of treatment at an early stage so patients can be treated in advance. The developed system is at its early stages with two fully working tools and shows promise for further development to increase its effectiveness and performance for complete professional use.
Stacking Ensemble Technique for Multiple Medical Datasets Classification: A Generalized Prediction Model

Precise early detection of diseases can reduce the worsening and lethality, but it is not a spontaneous act to deal with complex medical data. Machine Learning (ML) can help the research community extensively in this aspect by playing a vast role in predicting the status of diseases at early stages. The study intended to develop a generalized model based on ML techniques that can classify frequently occurring diseases with better performance and reliability. In this research, four datasets collected from different repositories, such as the MRI and Alzheimer's Dataset (MAD), the SPECTF Heart Dataset (SHD), the Early Stage Diabetes Dataset (ESDD), and Lower Back Pain Dataset (LBPD), followed by analyzing and evaluating according to their performances to propose the prediction model. Numerous studies on this aspect conducted by others are available, but there is still scope for prosperity. To overcome the shortcomings of previous research, we have driven the first step with data preprocessing followed by six classification techniques such as Logistic regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Extra tree (ET) are performed with 10-fold cross-validation as evaluation measure after assigning the best parameters manually by randomized search. In addition, the three best-performing classifiers (LR, RF, and SVM) are selected with their hyper-parameters to create an ensemble model through the stacking ensemble technique. After all, our generalized stacking ensemble model outperformed all other classifiers used in this study as well as other researchers in terms of accuracy that 96.97% in MAD, 95.08% in SHD, 98.90% in ESDD and 91.34% in LBPD are obtained.
Recogniition and Speech Conversion of Devnagri Script using CNN

In this paper, we are introducing an innovative, efficient, and real-time handwritten text-to-speech conversion technique for the Devanagari script. It combines the concept of Optical Character Recognition (OCR) and Text to Speech Synthesizer (TTS). This type of system can be helpful for visually impaired persons, for reading the number plates of a vehicle, and for medical applications. Text extraction from colored images is a challenging task in computer vision, for that, we have trained our model using Convolution Neural Network (CNN) with 1000s of different handwriting styles written by different people and of different age groups for each character. A trained model has been used for the recognition of input characters which was taken from the gesture movement of a fingertip with blue ink. The system is developed in Python 3.6. 0.
Sign-Sign LMS-Based Adaptive DFE for 4PAM Multi-Gbps Serial Links

In this paper, we present an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on the violation of received data symbols with minimum detection requirements at the signal-detection unit. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The validity of the proposed architecture is proved to utilize a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Specter from Cadence Design Systems with BSIM4 device models.
An IoT-based Weather Monitoring System for Upcountry Farming in Sri-Lanka

Agriculture, which continues to be the backbone of Sri Lanka’s economy, one of the fastest-growing in South Asia, employs more than 33% of all workers. The socioeconomic backdrop of Sri Lanka is significantly influenced by the agriculture business. Around 7% of Sri Lanka’s GDP would be accounted for by agriculture in 2019. Therefore, it is very important to analyzed agricultural problems and solve them. The goal of this study is to identify farming-related concerns in the upcountry region (Nuwara-Eliya, Badulla, Kandy, and Matale) and to recommend solutions to address these issues locally to support the socio-economic well-being of the population. The main problem faced by the up-country farmers is dramatic climatic change. The solution proposed in this research is a cutting-edge method for tracking local weather conditions and making the data available anywhere in the world. The creation of effective lot-based smart climate systems is a crucial demand for upcountry farmers in the sphere of farming. The proposed system makes use of sensors to track and control environmental variables including temperature and relative humidity and atmospheric pressure. It then can upload the data to the website, where it then plots the graphs of sensor data statistics. The latest information from the Internet users can access the installed system from anywhere in the world. Please note that this is a concept paper.
Intelligent Signal Control Module Design for Intersection Traffic Optimization

An intelligent control module for intersection traffic signaling is presented in this paper. Fundamentally, three control strategies (priority control, traffic light control based on stop lines and traffic light control based on lane connectors) are applied. The control module is designed for different types of intersections. When it is applied to a specific intersection, the road network of the intersection and its properties (e.g. lanes, directions, speed limits, stop lines, lane connectors, etc.) are imported into the module. The control module adjusts the control strategy as the arrival traffic flow changes day and night. Meanwhile, signal phases and times can be adapted to optimize the intersection traffic if the traffic light strategies are selected. Historical data on the traffic flow and control information can be used for further analysis and learning, to obtain more advanced skills in prediction and decision-making. Simulation results show that the control module may have better performance than any single strategy control method in reducing traffic congestion and increasing traffic efficiency at intersections. Furthermore, it is also designed for future extensible communications, to cooperate with other control modules and connected vehicles. Besides working together with traffic lights, the control module also provides for the virtual traffic signal application all control information from which connected users may receive the synchronized signals.
Longitudinal Control for closed loop simulation of Autonomous driving Vehicle

In level 2 automated cars, functioning breaks are currently present. New systems must be evaluated in a broad range of difficult scenarios in order to boost automation while assuring all-around safety. There are several disadvantages to validating these systems on real cars, including the time required to drive millions of kilometers, the danger involved in particular circumstances, and the high expense. Platforms for simulation show up as a suitable solution. In order to evaluate autonomous driving maneuvers and control methods, strong and trustworthy virtual environments are required. To that end, this study offers strategies which are created, adjusted, and verified using a custom simulation framework before being implemented in a real vehicle. A multibody vehicle model is used to calculate the simulation's dynamics. The usefulness of the suggested approach for creating and verifying longitudinal controllers for actual automated vehicles is demonstrated by a comparison of outcomes.
Career Support Platform for Older Adults Powered by AI

Global life expectancy has increased rapidly due to mankind's access to better healthcare and nutrition. In the past three decades, the average age of mankind has increased by 9 years. This contributes to many years in post-retirement life. While some might have built sufficient retirement corpus, many might rely on a post-retirement income for their livelihood. In addition, more and more people are open to retire early to pursue options in line with their passion. From an individual stand point, retirees prefer to stay physically and mentally active by engaging themselves in a full or part-time job. From the workforce perspective, the world will face a shortage of younger employees due to a low populated replacement rate, which in turn opens the doors for retirees to apply their experience and expertise across the areas of their interest. While there are many solutions focusing on students and the younger workforce to guide them on career options and job opportunities, there is a strong need to establish an ecosystem to support and counsel the aging population on their post-retirement career options. This paper focuses on an intuitive and intelligent AI-based platform offering holistic career guidance. Our solution is focused on using multiple Random Forest models and NLP techniques to provide suitable career options, available job opportunities, skills gaps, and learning paths to bridge the gaps.
Wind Speed Forecasting for Designing Sustainable Wastewater Treatment Plants

The power output from a wind turbine depends on the accurate predictions of the wind speed. Wind speed forecasting is also critical for installing wind turbines in wastewater treatment plants to lower the energy costs and carbon emissions. We use hourly meteorological data between 2011 and 2019 to predict the wind speed in the city of San Marcos, Texas. We explore several regression models including recurrent neural network, long short-term memory, and ensemble model, and compare their prediction errors to identify the best-performing model. Our study contributes to precisely anticipating wind speed prior to installing wind turbines in wastewater treatment plant, so that it will help in balancing other types of electricity generation, such as coal, natural gas, geothermal, and hydroelectric units.
High Impedance Fault Detection in Distribution Networks using Unsupervised Learning Technique

An algorithm is proposed in this article that detects High Impedance Faults (HIFs) in distribution networks using a novel approach. It is often difficult to detect HIFs in distribution grids because of the small magnitude of fault current and its non-linear variations. The purpose of this study is to investigate the possibility of automatically learning latent features from raw, unlabeled voltage and current data with the help of unsupervised feature learning. In order to identify transient behavior associated with HIFs and other typical faults, signal features are extracted using an autoencoder. A quadratic discriminant analysis (QDA) is then used to detect the presence of fault in the system. A fault detection accuracy of 98% is achieved by the proposed methodology. A modified IEEE 33 Bus benchmark system is used to validate the developed technique.
Microgrid Collaborative Control Method Based on Augmented Reality 3D Scene Reconstruction of Substation Operation Inspection

Due to the influence of impedance characteristics of micro grid lines and other factors, traditional droop control is difficult to accurately distribute reactive power according to droop gain, in order to improve the accuracy of reactive power sharing, the author proposes a distributed hierarchical cooperative control method. Based on communication technology and hierarchical control theory, a distributed hierarchical control architecture is established, including a primary control layer and a distributed secondary control layer. In the primary control layer, the traditional droop control virtual impedance control and PR voltage and current control are used to achieve power distribution. In the secondary control, the distributed consistency algorithm is used to construct the state observer, obtain the global average value, and adjust the deviation of voltage and reactive power in global consistency, in order to realize the voltage static error free control and the accurate distribution of reactive power, at the same time, it can reduce the requirements of the system for communication bandwidth, so that the system has higher reliability and scalability. Through the MATLAB/Simulink simulation experiment platform, the micro grid droop control system and the new virtual synchronous generator control system are built for simulation and comparison of control strategies, it is verified that the virtual synchronous generator has higher stability and security than droop control technology.
Dynamic Origin-Destination Demand Prediction with Improved LSTM Model

As the basis of dynamic traffic distribution, traffic demand prediction needs to address the complicated coupling correlations within the road network structure and the spatial and seasonal variation of traffic demand. Dynamic origin-destination (OD) demand prediction can present the correlation between nodes in the road network pattern and the time-varying and periodicity of traffic data. In this paper, we propose a dynamic OD prediction model considering the spatial-temporal and environmental aspects (STE) of the network (STE-CNN-LSTM) based on the Long-Short-Term-Memory (LSTM) algorithm. The proposed model consists of three modules including the spatial, temporal and external environment modules. The spatial module extracts the spatial correlation information of OD data. The temporal module extracts the time correlation information of OD data, and the external environment module deals with the impact of the external factors (e.g., weather, accidents) on OD demand. To verify the effectiveness of the proposed model, we conducted experiments using the OD dataset collected by the Caltrans Performance Measurement System (PeMS) of California in the United States, and the results show that the proposed STE-CNN-LSTM model performs better than the ARIMA, general LSTM and CNN-LSTM models in most respect in most respects.
Brain Tumor Classification Using Watershed Segmentation with ANN Classifier

A brain tumor is an uncommon form of body cell proliferation. The most difficult tasks in the medical profession are to identify and categorize brain tumors. A person's life may be at risk if the brain tumor is not immediately identified or diagnosed. In this proposed method, an artificial neural network (ANN)-based technique can classify brain tumors accurately. Firstly, the images are normalized using the scaling process. Then the normalized images are segmented using the watershed algorithm. After that, the seven statistical features are extracted and then applied as input to the ANN classifier for the classification of the brain tumors. The experimental result of the proposed method provides an accuracy result of 95.8% which is better than modern state-of-the-art methods. Furthermore, compared to other contemporary techniques, the chosen seven statistical features are comparably few in illustrating this performance.
Research Infrastructure for Real-Time Computer Vision Applications in Unmanned Aerial Systems

As the use and applications of Unmanned Aerial Systems (UAS) grow, the need for rapid and efficient research and development arises. UAS aspects such as sensing, navigation, and collision avoidance need to be evaluated. To facilitate this, research infrastructure comprising a simulator and a sizeable indoor testing center has been proposed and built. The discussed infrastructure aims to boost research progress with a focus on low-power computer vision solutions for UAS applications. This research and development process optimizes available resources by eliminating suboptimal solutions early. Leveraging a motion capture system, precise and accurate metrics can be used to evaluate and compare competing solutions prior to real-world testing. Additionally, various scenarios and environments can be recreated at relatively low cost in simulation or indoors, providing researchers with rigorous testing opportunities. Competitions are currently held to pit teams from around the world to produce effective solutions for UAS tasks. Beginning with simple problems, tasks of increasing complexity will be introduced commensurate with previous successes. While it is acknowledged that simulation and indoor testing cannot completely replace real-world testing, this infrastructure setup provides an invaluable opportunity for UAS researchers.
Device-Free Multi-Person Indoor Localization Using the Change of ToF

Indoor position information, which is difficult to obtain by GPS, can be used for various services and applications, thus indoor localization methods have been widely researched. Among them, device-free indoor localization does not require the target persons to possess a localization device, such as a smartphone, which can support the localization of all persons in the environment. Many methods using RSSI and Wi-Fi CSI have been proposed as device-free indoor localization using radio waves. However, RSSI is easily affected by environmental factors, such as multipath propagation. Wi-Fi CSI also has the disadvantage that there is no standard, so it relies on special hardware and software. Therefore, we propose device-free multi-person indoor localization using ToF information, which is less susceptible to noise. ToF information in indoor localization has mainly been used for highly accurate estimation of the receiver position, while this paper proposes a new application. In addition, distance measurements using ToF of Wi-Fi and UWB have been standardized and can be implemented with commercial equipment, which is highly practical. In this research, we considered the change in the distance measurement due to radio wave occlusion by a human body and built a device-free indoor localization system that can estimate multi-person position even if the model is trained on the data of only one person.
Performance Analysis of Linear Induction Motor Based on Field-Circuit Coupling

Aiming at the difficulty of electromagnetic performance analysis of linear induction motor(LIM) for rail in actual running, a three-dimensiona1(3D) finite element electromagnetic simulation model of 12 kW linear induction motor is established. Combined with the experimental results of the prototype, the differences between the 3D finite element model and the twodimensional (2D) finite element model in performance analysis are compared and analyzed. In addition, the field-circuit coupling model of the LIM vector control system considering the transverse and longitudinal end effects is established by using multiple simulation platforms, and the motor uses the 3D finite element model. The running performance and loss curve of the motor under the vector control system are analyzed. The simulation and experimental results show that, the simulation results of the 3D model are closer to the experimental results, which reduces the error to a large extent. Under the control system, the motor runs stably, and all kinds of electromagnetic performance meet the design requirements. The analysis method of multi-platform cosimulation to solve the motor performance provides a reliable means for the design of the motor body and running adjustment under the control system.
An Efficient Modulation Strategy for Modular Multilevel Cascaded Inverter Used in Solar PV Fed Induction Motor Drive Systems

In this research work, a new modulation technique is proposed to control the switching of a 3-phase modular multilevel converter (MMC) based solar photovoltaic (PV) fed induction motor (IM) drive system. Multilevel inverters (MLIs) are gaining popularity in the industry as medium-voltage and high-power electronic power conversion solutions. Different multilevel inverter topologies have grown in prominence in recent years, owing to a variety of advantages, particularly in induction motor driving systems. Inverter switching strategies are critical for improving power quality. In this paper, a new switching method for a 5-level MMC is proposed that ensures high power quality, improves speed and torque performance, and reduces total harmonic distortion (THD) in the voltage and current waveforms of the stator of a PV-based IM. The practicality of this modulation method is demonstrated by comparing its performance to that of several existing popular switching strategies. The design, implementation, and comparisons are done by using MATLAB/Simulink simulation. A laboratory-scale prototype is developed and tested to evaluate the performance of the proposed switching technique.
Prediction of Cardiovascular Diseases Using Machine Learning Algorithms

The heart is the most vital organ of any living being. It gives life and ensures blood reaches the body. And yet heart diseases are the leading cause of death these days. Heart Diseases not only involve strokes and heart attacks but also lesser-known yet deadly diseases like (like (i) Aortic stenosis - a condition in which the aortic valve, which regulates blood flow out of the heart, becomes narrowed, making it difficult for blood to flow through; (ii) Pulmonary hypertension - high blood pressure in the pulmonary artery, which can cause the right side of the heart to become enlarged, leading to heart failure; and Atrial fibrillation, Mitral valve prolapse, Hypertrophic cardiomyopathy, etc. What makes these diseases more deadly is the fact that these are asymptomatic diseases, i.e, they may not show the symptoms until it is too late. Hence requiring regular check-ups from doctors. But even still there is a very high chance of error in predicting the disease accurately due to factors such as (i) expensive tests that most people can’t afford, (ii) Misinterpretation of test results, (iii) Failure to consider the patient’s previous symptoms and history, which could be due to patients not disclosing shameful history with doctors. Many of the errors can be narrowed down to the possibility of human error, which is what we are trying to eliminate through this paper. In this paper, we are going to see how we can improve the efficiency of predicting heart diseases. We are going to see what effect using cross-validation and feature selection methods are going to have on our accuracy. In this paper, we analyzed the efficiency of machine learning algorithms like SVM, Decision Trees, Random Forests, and Neural Networks in predicting heart diseases using the confusion matrix. Through the analysis, it was found that the Random Forest model provided the best prediction accuracy of 83%.