Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Approach for Human Stress Detection using Haar-Cascade Algorithm

Human emotion and psychological stress are closely related to one another. According to research in computational psychology, knowing how stress and emotions interact is crucial. Deep learning has been used in research to identify facial expressions from photos, but finding psychological stress has not yet been directly addressed. In this study, a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described. An HCA is taught to identify distinct emotional categories in human faces and detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise). For detection, on a huge number of both positive and negative images, a cascade function is learned. The deciphered emotions are then used to evaluate stress via further logarithmic regression. To assess the proposed design, experiments have been carried out using the Facial Expression Recognition (FER2013) dataset.
JUST TELL ME: A Robot-assisted E-health Solution for People with Lower-extremity Disability

Robotics technology has been increasingly applied to healthcare contexts to enhance efficiency and safety in healthcare processes in recent years. People with mobility impairments and disabilities often require caretakers in their lives. Fortunately, robots can provide attention and assistance consistently for them instead of human caretakers. Motivated by this, we develop a robot-assisted e-health solution to empower the patients' daily lives and improve their wellbeing in this study. A transfer learning-based approach is proposed to train the robot to understand and identify patients' needs through a small dataset. Using the proposed approach, the robot is able to understand the patient's needs through speech recognition and recognize objects that the patient has requested. The proposed solution is experimentally implemented in real-world human-robot interactive healthcare contexts. Results and analysis indicate the success and accuracy of our approaches.
Recognition of Tamil handwritten characters using Scrabble GAN

Recognition of hand-or machine-printed documents is now a crucial component of applications. A commonly used method for converting a printed or handwritten file into its equivalent text format is Optical Character Recognition (OCR). Handwriting recognition is a major research area in pattern recognition and attracts extensive research in OCR. In Southern part of India, Tamil is the primary language with the longest continuous literary heritage. OCR is used to extract characters from scanned digital input images and convert them into formats that can be edited by machines. It is quite challenging to recognize handwritten Tamil characters due to variations in font size, orientation angle and writing styles, bigger category sets, and misunderstanding over the similarities between handwritten characters. In this study, we suggest a deep learning-based technique for handwritten Tamil character recognition. Text documents printed on paper require time-consuming character correction and reprinting. Here, a model incorporating scrabble GAN with CNN is used to increase the dataset. New training instances are created from the old instances using Scrabble Generative Adversarial and Networks augmentation to improve the accuracy of detecting the Tamil handwritten characters. The primary goal of the research work is to expand the handwritten characters and recognize them. Hyperparameters such as kernel size, activation function, padding, step size and layer count are adjusted to enhance CNN-based handwriting recognition. In order to distinguish between characters, CNN evaluates their differences in shapes and features. Dataset consists of nearly 6,000 images and the 80% of the images are used as training images and 20 % of the images used as testing images and obtained the accuracy of 96.23%.
Docker Networking: A Security Review

The Internet has become an integral part of our daily lives, and Cloud Computing has become the buzzword of today's generation. In order to achieve virtualization, cloud computing has relied on tools like virtual machines and containers, with containers making the transition due to their faster startup time and increased scalability. This paper provides an overview of Docker, which has recently emerged as the most well-known containerization solution. The different varieties of Docker networks and how they could provide a security risk if not maintained properly have been described. It is followed by a case study demonstrating the ease of carrying a spoofing attack on the Docker default network. The use of encryption and a limited capability set to secure internal communication in a Dockered application has also been emphasized.
Machine Learning-Based Crop Recommendations for Precision Farming to Maximize Crop Yields

Technology is now playing a critical role in many industries to overcome challenges and achieve stronger and maximum results. The farming industry in India significantly affects the country's economy. The agricultural sector continues to employ half of the nation's workforce. The natural conditions of the environment have a big impact on the agriculture industry, which creates a number of difficulties in actual farming operations. The majority of agricultural practices in the nation still use outdated methods, and technological advancement is slow. Appropriate technology can be applied to this industry to maximize yield and minimize challenges. Rather than considering the parameters like soil conditions, sustainability, etc., farmers frequently sow the crop based on its market value and potential financial profits. This might have unfavorable effects on both soil quality and farmers. If used properly, modern techniques like machine learning techniques have the potential to revolutionize these fields. This paper will demonstrate how to use such technologies effectively to offer farmers the best crop recommendation support.
Multiple Imputation by Chained Equations for Missing Data in UK Biobank

UK Biobank is a large cohort study and faces missing data problems. The complexity of observations in this dataset, including noisy data, different missing rates, and the diversity of the data distributions and data types, results in the challenge of imputing missing data in the dataset. With the aim of addressing this issue and imputing missing values in UK Biobank, we propose an imputation framework based on prior knowledge and multiple imputation by chained equations (MICE), which consists of three parts: (1) Data cleaning for eliminating the interference of noisy data, (2) Correction of imputation illegibility for high missing rate subjects and low variance variables, and (3) MICE for imputing different types of independent variables. By comparing the imputation results of linear regression, linear regression with bootstrap, Bayesian linear regression, and random forest for continuous and categorical variables, we find that the best imputation model for continuous variables is linear regression, with the normalized mean absolute error (MAE) of 0.072+/-0.004 in the experiment of actual missing percentage, while the best imputation model for categorical variables is random forest, with the normalized MAE of 0.129+/-0.003. By comparing the imputation results of logistic regression, logistic regression with bootstrap, and random forest for binary variables, we find that the best imputation model is random forest, with the accuracy of 0.907+/-0.008 in the experiment of actual missing percentage. In addition, the data cleaning improves the imputation accuracy by 6.83% overall. The correction of high-missing rate variables is also a significant step, the imputation accuracy of all types of variables is 0.842+/- 0.006, 0.826+/-0.006, 0.793+/-0.005, 0.766+/-0.007, and 0.742+/- 0.006 when the missing percentage is 50%, 60%, 70%, 80%, and 90%, respectively.
Innovative Method for Face Emotion Recognition using Hybrid Deep Neural Networks

Automatic facial expression recognition opens up new possibilities for human-computer interaction. To a large extent, face detection is the crux of many systems that aim to recognize people. Face detection is a challenging task because of the wide variety of human faces in terms of color, stance, expression, location, and orientation. It is therefore convenient to distinguish different facial expressions using various modelling techniques. Recognizing human gestures in images is a fascinating area of research because of the variety of expressions that may be conveyed by the position and movement of the eyes, mouth, nose, etc. Any facial expression-recognition neural network can also be used to classify faces, identify faces, and match faces to tokens. The FER system is entirely automated, with separate modules for finding faces, finding faces, extracting features, choosing the best features, and classifying data. After a face has been detected using the AdaBoost algorithm, the frame with the highest intensity of emotion is extracted using the inter-frame mutual information criterion. Several techniques, such as the Gabor filter and GAN-CNN model training are applied to the selected frames to extract distinctive characteristics. According to the findings of this study, a GAN-CNN based method can be used to identify the face expressions. As compared to the CNN and SVM models, this approach achieves higher accuracy (about 99.7%).
Greenhouse smart irrigation based on soil moisture and vegetation index measurements

Water is the most critical resource for agriculture; therefore, efficient water use is one of the most relevant challenges that precision agriculture must address. Greenhouse agriculture is a promising approach to evaluate alternatives to improve water efficiency since the different variables can be monitored within a controlled environment. Among these agriculture variables, soil moisture and vegetation index provide relevant information to make farming decisions that contribute to agriculture sustainability. Soil moisture measures the amount of available water for the crops, while the vegetation index indicates the healthiness level of the plants. Available sensors allow the measurement of these variables while the crops continue growing, and Internet of Things technology provides the tools to implement automated greenhouses. This paper evaluates different irrigation scheduling techniques and water stress mitigation strategies to analyze alternatives to improve agriculture water efficiency within a controlled environment.
Design of Single-Stage Light Electric Vehicles Battery Charger Based on Isolated Bridgeless Modified SEPIC Converter with Reduced Switch Stress

The segment of onboard chargers (OBCs) is dominated by two-stage charger topologies, which have several disadvantages like bulky size and less efficiency in view of high component count, high cost and complex controller design. To overcome these disadvantages, an isolated bridgeless version of modified SEPIC converter is used to develop a single-stage high power factor battery charger for light electric vehicles (LEVs) in this study. Apart from the advantages of high power factor operation, continuous input and output currents, and high voltage conversion ratio, the modified SEPIC converter based charger is designed to reduce the voltage stress on the power switches in the converter circuit. The presented charger operates in discontinuous conduction mode (DCM), which endows it with several advantageous features such as innate power factor correction (PFC) capability, concession in control effort, decrease in size of magnetic components and reduction in sensor count leading to discount in overall cost of implementation. The paper attempts to elucidate the operation of the charger, design of charger components, design of control algorithm and performance validation of the designed components and control logic by means of simulations. Viability of 48 V LEV battery charging under constant current (CC) mode is explored in this study for a 500W rated charger.
Brain Tumour Segmentation Using Wasserstein Generative Adversarial Networks(WGANs)

The majority of health-related applications use technology and require a lot of data to operate. Even more, information is required for brain tumor segmentation, but many people lack access to it because of privacy laws governing medical information. Therefore, this study utilizes GAN technology, which creates synthetic images to resolve this issue. AGGrGAN is used for aggregation and discrimination, and PGAN, WGAN, and DCGAN are used to create synthetic pictures. Synthetic brain tumor masks with the same visual characteristics as the real samples. potential benefits of using GANs to synthesize new images from a real dataset of brain MR scans and corresponding hand-labelled segmentation masks (annotations), and an evaluation of the potential performance gains that are achieved when using synthetic images to augment a dataset that is used to train a neural network to solve a brain tumour segmentation task.
Low Overhead System-Level Obfuscation through Hardware Resource Sharing

Logic locking techniques have been proposed to protect chip designs from malicious reverse engineering and overproduction. Stripped functionality logic locking (SFLL) has gained substantial traction as a current state of the art method, exhibiting strong resilience against a wide variety of attacks. However, secure instances of SFLL-based locking tend to have high power and area overheads, particularly in its restore units. This work presents a novel architectural approach to restore unit configuration for SFLL-like logic locking methods that treats restore units as an overhead-constrained shareable resource. We describe how resource contention caused by sharing of restore units imposes constraints on the underlying locking scheme from a graph theoretic perspective and propose both a 0-1 ILP and a heuristic clustering algorithm for finding resource-constrained shared locking configurations that satisfy these constraints. We evaluate our sharing method on SFLL-flex and find that our ILP and heuristic methods were each able to achieve a 55% and 31% reduction in power used by locked datapaths synthesized from MediaBench benchmarks while maintaining the same security and functionality compared to datapaths locked with conventional gate-level techniques.
A CNN Based Machine Learning Scheme to Detect Lung Cancer Detection from CT Scan Images

The major cause of mortality in today’s globe is lung cancer. As a result, respiratory organ nodules, which are round or oval-shaped growths of scar tissues, must be identified properly and quickly in order to prevent lung cancer from developing. As a result of critical tasks, oxygen to the human body is provided by lungs while carbon dioxide is excreted; require early identification of malignant cells. In order to reduce the chance of mortality, it is critical to begin therapy by detecting cancer using a CT scan. The suggested technique involves changing the parameters, combinations that are dynamically pooled, dropout procedures that are added, and linear unit functions that are corrected (ReLu) to build a model for detecting lung cancer and categorizing tumors discovered in the lungs as malignant or benign. To forecast greater accuracy, this projected method is compared to the pre-trained model VGG16. This scheme will make it easier for doctors to give early treatment and reduce mortality.
Design of Vivaldi Array Antenna for Hydrogen Line Observation and Weather Radar Application

A low cost and low profile $2\times 2$ Vivaldi array antenna is designed and developed to observe and detect the 1.42 GHz hydrogen line emission from the space. Firstly, the single element of Vivaldi antenna is designed at operating frequency of 1.42 GHz which obtains the directional pattern with the average gain around 4 dBi. To achieve high gain and decrease HPBW, the $2\times 2$ Vivaldi array antenna is introduced and each element receives signal through 1 to 4 Wilkinson power divider. The proposed array antenna obtains 60 deg-HPBW with low side lobe level. Moreover, the average gain is around 9.5 dBi for measurement with the clear properties of linear polarization. Finally, the proposed Vivaldi array antenna is going to apply and employ as receiver for hydrogen line spectrum and also weather radar application.
Ultra-Wideband Power Divider with a Controllable Notched Band

ABSTRACT In this article, an ultra-wideband Wilkinson power divider with a controllable notched band has been presented based on the multi-section Wilkinson structure. Analyzed by the even–odd mode method, a triple-stub loaded resonator is embedded in the power divider to introduce a notched band for which the center frequency can be easily adjusted by only one structural parameter. The simulated and measured results show that the return losses of the proposed power divider are better than 10 dB, and its isolation characteristics are larger than 18 dB from 0.1 to 8 GHz regardless of the notched band.
Distributed Inference Approach on Massive datasets using MapReduce

Contemporary computer systems and applications generate high volume of data every day. Gaining knowledge from this ever-growing high velocity and high volume data is crucial to have insights and business intelligence. Using semantic web approaches for generating inferences to gain knowledge have been quite successful. When processing large amounts of data, a centralised method for finding inferences in ontologies will be ineffective. Therefore, to solve this problem, a distributed strategy is needed. The major challenges on large scale data are the difficulty in deriving suitable triples for appropriate inferences, to reduce the time spent in processing of inference and the requirement of scalable computation capabilities for large dataset. Also, storage space for increasing data must be addressed efficiently. This paper proposes a distributed conjecture approach to address the above issues by construction of SIM (Sparse Index Method) and ATC (Assertional Triples Construction) and to efficiently process the users’ queries.
Study of EMG-based Mouse Clicks Type Detection

The operation of a computer required humans to use several parts of their body. However, there were some conditions where humans cannot operate computers correctly or in a normal position; examples of these conditions were accident victims and people with disabilities. Therefore, a system was needed to help make it easier for these people to operate the computer. This study developed a system that can classify click types using EMG sensors, the K-NN method, and the SVM method. EMG sensors helped take data in the form of signals from human muscle contractions which will later be classified into left-click and right-click. At the same time, it was useful for classifying these types of clicks for the K-NN and SVM methods. Data from EMG sensors were trained using the K-NN and SVM methods using 54 data sets in each class, namely left-click and right-click classes. The K-NN method was trained using k=3, 5, 7, 9, and 11. The SVM method used linear kernels, Radial Basis Function (RBF), polynomials, and sigmoids. After that, the accuracy values of the two methods will be compared. The study has successfully classified the types of clicks based on the input from the EMG sensor using the K-NN method with the highest accuracy results using k=3, which was 81.81%, and the SVM method using polynomial kernels which were 84.84%. The highest accuracy value was obtained by comparing the two methods, namely using the polynomial kernel SVM method. Adding datasets and conducting experiments using other methods as further comparisons can be used to improve system accuracy.
An Investigation of Multi-Pattern Load Altering Attack Detection*

Load-altering attack (LAA) is an important class of cyber-physical attacks against demand-side management (DSM) systems. Previous LAA studies mainly target the detection and mitigation of unique LAAs. In this paper, we consider more general LAA types by assuming that attackers can either perform random or goal-oriented LAAs against single or multiple load buses. We first make an exploration of different types of LAAs and the corresponding models, based on which we synthesize compromised power flow data and design a multi-class classification-based detector to distinguish among different LAA patterns.
A Warehouse Storage and Retrieval System Using IoT and Autonomous Vehicle

In order to effectively manage logistics and the supply chain, warehouses must adapt to the rising demands of information integration, speed and precision. As a viable choice for the growth of warehouses in the future, the “smart warehouse” system, which is linked with emerging warehousing innovations, is progressively attracting the attention of technology and industry elites. In this study, we have designed an Autonomous Vehicle-based Storage and Retrieval (AVS/R) system that receive, store, retrieve, and ship items in a warehouse. The design is based on using an Autonomous Guided vehicle that navigate through the warehouse landmarks, to transport items between I/O Port and the Storage Unit. The system components communication is based on the Internet of Things publish/subscribe massaging protocol MQTT also ensuring remote monitoring and control of the process through data stored in cloud. Overall, the proposed solution provides a flexible, scalable, efficient, and integrated warehouse solution.
Design and Control of a Silicon Carbide (SiC)-Based Isolated Full-Bridge Converter with Current-Doubler Rectifier

This paper presents the detailed design and analysis of an isolated SiC-based full-bridge converter with a synchronous current doubler rectifier at the output side, achieving high power density for applications like auxiliary power supplies for Electric Vehicles (EV). This paper also proposes the design of a voltage-mode control of the converter, which has been implemented in the digital domain using MAX10 FPGA through VHDL. The internal ADC of the FPGA has been used for sensing purposes. The operation, design and small-signal modeling of the presented converter are described in this paper. The laboratory developed prototype of the converter has been tested at 48V regulated output, 730V input with 150kHz switching frequency and up to 4kW power output. The FPGA-based controller achieves a much lower output voltage settling time of $120\mu \mathrm{s}$ and undershoot of 9.5% for an 8.5% step increase in load. The dynamic response is 6 times faster than literature reported similar converters. The power density achieved for 4kW output is 58.53 $W/inch^{3}$, which is 16% higher than the similar converters mentioned in the literature.
Smart Traffic Monitoring System using YOLO and Deep Learning Techniques

As the world's population grows, there are more vehicles on the road every day, which leads to an increase in heavy traffic. Traffic monitoring is essential for preventing accidents. To detect reckless drivers and other traffic infractions, a model that can track, identify, and categorize vehicles is needed. The task of counting the number of vehicles is crucial in traffic situations because it allows the authorities to prevent accidents and traffic jams caused by heavy traffic. The approach outlined in the study uses the image processing methods YOLO and OpenCV to count the number of vehicles, classify them, and identify them. By processing the images from the input video given to OpenCV, a software library, the objects are detected and identified. In comparison to other object detection algorithms, the real-time object detection algorithm YOLO is both quicker and more accurate. The accuracy and efficiency of vehicle detection and classification have been greatly enhanced by convolutional neural networks and other machine learning algorithms, enabling real-time analysis of enormous amounts of data. With the help of this technology, driving safety will be increased, traffic flow will be optimized, and autonomous driving will be made possible.