Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Dyslexic Children Using Machine Learning and Multimodal Hindi Language Eye-Gaze-Assisted Learning System

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based virtual keyboard (VK). In this study, we propose to use the typing performance of a multimodal Hindi language eye-gaze-assisted learning system based on a VK to help in the reduction of tracking errors for people with writing and reading deficiencies and to detect children with dyslexia. Performance was assessed at three levels: eye tracker, eye tracker with soft switch, and touchscreen as a baseline modality using a predefined copy-typing task. The system was validated through a series of experiments with 32 children (16 dyslexic and 16 control). The results show that the workload and the usability of the system are substantially different for children with dyslexia. Children with dyslexia have a lower typing performance when using the touchscreen modality or the eye tracker only. The detection of children with dyslexia from others was assessed with seven different types of classifiers using the typing speed on different words (AUC $>$ 0.9). These results highlight the need to have fully inclusive VKs. This work demonstrates the superior use of a multimodal system with participants having unique neuropsychological conditions and that the proposed system can be used to detect children with dyslexia.
Identification of Flowers using Machine Learning

There are so many flower species in the world today that registering and identifying them all is difficult and time-consuming. Every day, we notice a large number of flower species in our houses, parks, roadsides, fields, and on our rooftops, but we have no idea what those flower species are or where they came from. We don't even know what it's called. There are various guidebooks for flower knowledge however, finding the name when you have a picture can be challenging. However, it is impossible for the human brain to remember all of the species it encounters. In that case, we will use this concept to conduct research and develop our project. That will inform people about an unknown flower that they have seen but are unaware of. Machine learning for flower classification can pave the way to a solution. Flower classification is a system for identifying and researching specific flower types. Our technology will advance in identifying specific flowers on the spot. The goal of this system is to help those who want to learn about flowers: botanists, travellers and environmental enthusiasts. The proposed flower detection system is implemented by developing a convolutional neural network, which is a very efficient model for image classification. Convolutional neural networks are a popular machine learning domain that is commonly employed in image classification. As a result, we chose this topic to research picture recognition using CNN and Tensorflow. Though we created a web application for our research project, we have many plans to conduct an additional study in order to improve our system.
Security Model for Securing Data on Cloud

Data storage is transitioning from physical servers to “cloud” storage because cloud service providers deliver the resources through the Internet when required. Cloud computing is a successful and rapidly evolving technology. With new features and capabilities released regularly, companies can move to the cloud on a “pay as you go” model. The most prominent feature of pay as you go model on the cloud is that it is cost-effective, however, this makes the cloud platform vulnerable to cyber attacks. This is the reason, the safety of such information has become a concern. This paper provides a security framework that secures cloud storage from unauthorized individuals by using user identification and authorization. We have used dual encryption by using Advance encryption standard (AES) & Ecliptic Curve Cryptography (ECC). The double encryption makes the cloud environment resilient against different security attacks. The experimental results show that the proposed model has low computing costs.
Image Processing based ML Framework for Leaf Classification and Disease Detection

Image processing techniques are employed in this paper to supplement machine learning models for categorizing leaf species and detecting unhealthy spots in them. Multiple public datasets were mixed with a self-curated dataset of leaves from a homemade garden for the same. A set of 17 distinct features are extracted from background-subtracted leaf photos to capture information about texture, colour, and shape for leaf classification. These characteristics are used to train machine learning models such as Support Vector Machine and K-Nearest Neighbors, which do not require hardware intensive training. These models are trained on several dataset combinations, including the merged one, and then compared using precision, recall, and F1-score measures.
Chaotic-Map Based Encryption for 3D Point and 3D Mesh Fog Data in Edge Computing

Recent decades have seen dramatic development and adoption of digital technology. This technological advancement generates a large amount of critical data that must be safeguarded. The security of confidential data is one of the primary concerns in fog computing. As a result, achieving a reliable level of security in the fog computing environment is crucial. In this context, 3D point and mesh fog data are becoming increasingly popular among the various types of data stored in the fog. Data encryption using chaotic behavior is one of the preferred research areas due to its unique properties, such as randomness, determinism, sensitivity to initial conditions, and ergodicity. In this paper, we have taken advantage of this chaotic behavior to achieve higher security. This study presents a novel approach for protecting the privacy of 3D point and mesh fog data. Initially, the fog data coordinates are transformed using the sequence generated by the chaotic behavior. Then, bifurcation analysis is used to depict the enhanced scope of the proposed map. The quality of the proposed chaotic system is assessed using metrics such as the Lyapunov exponent and approximate entropy. Results show that the proposed encryption framework performs superior when subjected to brute-force and statistical attacks. Further, the designed framework produces better results than the prior literature.
Design of a Cascade Stacking Ensemble Algorithm Based on Multi-domain Feature Fusion of MI-EEG

Electroencephalogram (EEG) signal is a complex bioelectrical signal with non-stationary, nonlinear, high dimension, and low signal-to-noise ratio. Single time domain or frequency domain analysis has great limitations, and the wavelet transform combined with time-frequency analysis cannot completely distinguish the artefact components like the EEG signals. All these problems will reduce the classification recognition rate. In view of this, an ensemble learning algorithm based on multi-domain features is proposed to realize the classification of MI-EEG. Four complementary features of time domain, frequency domain, spatial domain and time-frequency feature are calculated and fused as feature vectors, and then classified by an ensemble algorithm. Then, a cascade classification model based on a Stacking integrated algorithm is proposed. The first level is composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Extreme Gradient Boosting (XGBoost) model, and the second level is composed of logistic regression (LR) to output the results. The output results show that the classification results of fusion features are significantly improved compared with those of single features. the accuracy of the Stacking integrated algorithm is up to 90.12%. Compared with a single classification algorithm, the average classification accuracy is improved by 3%.
Motor Imagery based EEG Signal Classification using Multi-scale CNN Architecture

Deep learning has been employed in EEG signal classification in recent years since it has multiple advantages, including automatic feature extraction and classification. Although deep learning models have been used recently for motor imagery signal classification, still there are challenges to improve the performance of classification. One of the challenges is the small size of the training dataset. Also, most of the existing CNN models utilize the features extracted from the last layer, which might result in the loss of some specific details. A multi-scale CNN architecture is proposed in this work to utilize features at the initial and final layers for classification of MI signals. Firstly, the size of the dataset is increased by using the sliding window data augmentation technique and time-frequency images are obtained after applying short time Fourier transform (STFT). The images are then fed to the multi-scale CNN. A mean kappa value of 0.803 and a mean classification accuracy of 85.3 percent is achieved for this model on dataset 2a of BCI competition IV through subject-specific training.
Digital Forensics for Medical Internet of Things

Medical Internet of Things (MIoT) Forensics is an important subject that has received little attention. With the advancement of the Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) applications, a large amount of data gets generated with increased complexity. Consequently, it gives rise to cyber-attacks and data theft. The consequences of these malicious activities can be life-threatening. Therefore, organizations must identify these threats to implement proactive and reactive measures. Digital Forensics of medical devices focuses on reactive measures to generate evidence of unprecedented events presented in court. Digital forensics, in combination with ML, might aid in the development of a secure MIoT system that can detect cyber-attacks and provide evidence for the legal process. Implementing digital forensics in medical systems is challenging, given their complex functioning. This paper presents a novel methodology for developing a forensic-ready MIoT system with two distinct approaches: an intrusion detection system and modeling of the physiological data. In addition, we cover various digital forensic processes for MIoT systems to log evidence of malicious activity at various system levels. This research highlights the importance of digital forensics for MIoT privacy assurance.
A Cubature Kalman Filtering Algorithm for Robot Pose Estimation

To maintain stability while executing the walking paths, robots have to know its own pose. However, the visual sensor of the visual/inertial navigation odometer (VIO) system is easily affected by the environmental and lighting conditions, and the measurement data of the Inertial Measurement Units (IMU) have a drift problem caused by accumulated errors, which makes the VIO system transmit the robot rotation uncertainty inaccurately. Aiming at the defects of vision sensor and IMU in robot positioning, to effectively improve the accuracy and stability of rigid body pose estimation while eliminating system internalization errors, a matrix Lie group representation based Cubature Kalman filter (CKF) VIO framework is proposed in this paper. By utilizing the high-dimensional matrix Lie group, which is composed of IMU's position, orientation, velocity and spatial three-dimensional (3D) feature points, to represent the system state variables, the accurate estimation of the robot's pose is achieved under the CKF framework. This paper derives the motion model of the IMU and the camera observation, and describes the process of constructing the Cubature point of the exponential map when the state vector dimension is high, and also transfers the state uncertainty and observation update. Finally, pose estimation accuracy verification experiments are carried out. Results show that the proposed algorithm not only inherits the advantages of the invariant extended Kalman filter (In-EKF) in terms of convergence and consistency, but also avoids the complexity of matrix solution process, improving the accuracy of robot pose estimation effectively.
Recent Advances and Challenges of Edge AI and IoT Assisted Covid-19 Alike Detection Systems

The current era is a time of technology and without acknowledging the role of technology in the different sectors of life, advancement will remain at a slow pace. Artificial intelligence (AI) in the field of science is now being used in almost every field whether it is medicine or any other. The research aims to find out the recent advances and challenges of Edge AI and IoT-assisted COVID-19 alike detection systems. The current study aims to conduct a systematic review by selecting secondary articles. Therefore, out of348 articles, only 7 articles were taken to conduct a literature review. The health care industry must understand the role of IoT in proving the fast pace results in medical through the adoption of the technology. The perception of the people regarding the technology is also highly supportive and the industry is demanding more AI-integrated systems through which advancements will be made in this sector. there are several aspects of the Edge AI and IoT due to which the patient care, covid-19 like a pandemic, and the detection system alike to covid-19 virus detection system will be effectively providing benefits to the people on a long-term basis. The finding of this research stated that the advancement in technology and the usage of Edge AI in medicine is very crucial to understand with better recognition of the benefits which are provided through it.
Analysis of Deep Learning Algorithms on Edge in Microscopic Fabric Dataset

Vision Intelligence has been applied to textile industries for handling complex situations. The chances of performance deterioration are always present in manual inspections because of vigilance and human errors. This paper describes the model-based texture classification of different types of standard fabrics on edge device. In this paper, we proposed a method to classify three different categories of fabrics using deep learning models on the self-made dataset using a 1600X LED Magnifier Handheld Digital Microscope and Raspberry Pi 4 Model B. Intensity distribution, pixel-based, region-based, Gabor filters, Gaussian mixture-based models, wavelet transform, and neural networks were popular in texture classification. In the learning phase, we used six different models to have accurate results. All the training results are ranked sequentially based on extensive testing. The process is automated due to the vision-based categorization of textures.
Retinal vascular segmentation algorithm based on Atrous attention U-net

The segmentation of retinal blood vessels is helpful in the diagnosis of diabetes, hypertension and other diseases. However, retinal blood vessel segmentation has become a big problem due to different vessel sizes, complex vessel structures, and limited annotated data sets. Deep learning is widely used in the field of medical image segmentation, among which U-net is widely used due to its good effect in small target segmentation and its scalable structure. In this paper, an improved model based on U-net, namely Atrous Attention U-net (AAU-net), is proposed, in which the Atrous convolution module and attention gate are added. Compared with the ordinary convolution module, the Atrous convolution module expands the receptive field and improves the correlation between objects. Before splicing the corresponding features in encoder and decoder, an integrated attention gate (AGs) is added, which can highlight the significant features transferred by skipping the connection. A series of experiments on the public DRIVE dataset show that the performance of this method is improved compared with the traditional U-shaped network, and the structure is relatively simple compared with other improved methods.
Intrusion Classification for Cloud Computing Network: A Step Towards an Intelligent Classification System

One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic
Highly Sensitive Photonic Crystal Fiber based Sensor for Sensing Nitrous Oxide (N2O) and Benzene (C6H6) Gas Present in the Atmosphere

Photonic crystals have some unique properties if any optical fiber depends on those fundamentals it is meant to be a PCF. It is designed by using the three major elements such as core, cladding, and the PML. Here, the detection Nitrous Oxide (N2O) and Benzene (C6H6) Gas present in the atmosphere is done by designing a highly sensitive Photonic Crystal Fiber based sensor. There are many ozone depleting elements in that one of the major harm is due to (N2O) gas and it even causes many health defects to living organisms such as depression, hypoxia, psychosis, damages immune system, and loss of memory. Basically, there is a presence of (C6H6) gas in the atmosphere these elements react with the chemical compounds creating smog which breaks down at some point and then the collision between rain and snow happens resulting in contamination of water and soil. It also results in harming the human beings by creating problems such as increasing heart rate, unconsciousness, level of RBC gets decreased, and damaging the bone marrow. Due to all these reasons, at the present scenario it is very much needed to sense the Nitrous Oxide (N2O) and Benzene (C6H6) Gas at atmospheric level. The highly sensitive proposed model is designed by using the COMSOL Multiphysics software by obtaining an accurate and high performing output. The simulation results are calculated by using the final element method which results in the relative sensitivity of 99.384%, 99.937% and 9.56 × 10−11 dB/m, 0dB/m loss of confinement at the wavelength of λ = 1μm, 0. 20μm for N2O and C6H6 Gas. According to the above results it is proved that the proposed gas sensor is very simple with high rate of performance and accuracy.
DCA-Net: A W-shaped network for segmenting COVID-19 infected area from medical images

Coronavirus disease (COVID-19) poses a significant threat to humans in 2019. Automated and accurate segmentation of the infected region of COVID-19 computed tomography (CT) images can help doctors diagnose and treat the disease. However, the variable shape of COVID-19 infected areas, which can be easily confused with other lung tissues, poses a challenge for CT image segmentation. To address this problem, a deep learning-based convolutional neural network is proposed for the automatic segmentation of COVID-19 lung infection regions. Our proposed segmentation method uses a U-Net network as the backbone, constructed as a coarse to fine segmentation network. Firstly, we introduce our designed contour-enhanced module (CA) in the coarse segmentation network to effectively extract the lung region; secondly, we introduce our designed multi-scale feature attention module (MFA) in the fine segmentation network to enable the network to extract spatial efficiently and channel information, better focus on quantifying the effective region, and enhance the network segmentation effect. Using the COVID-19 public dataset, the proposed network achieves the best segmentation results. The Dice, IOU, F1-Score, and Sensitivity metrics reach 88.74%, 78.73%, 86.58%, and 88.16%, respectively. DCA-Net can efficiently segment the COVID-19 infected region, which can be of great clinical benefit.
The anomalous abundance of cosmic ray nitrogen and oxygen nuclei at low energies

Recent measurements using the Goddard-University of New Hampshire cosmic-ray telescope on the Pioneer 10 spacecraft have revealed an anomalous spectrum of nitrogen and oxygen nuclei relative to other nuclei such as He and C, in the energy range 3-30 MeV per nucleon. The intensity of nitrogen and oxygen nuclei is enhanced by a factor of up to 20 relative to their abundance in galactic or solar cosmic rays. It is argued that this is most likely a new extrasolar component of cosmic rays.
Diabetic Retinopathy Detection: Improving Accuracy Using Multiple Transfer Learning Features from Pre-trained Deep Learning Networks

Diabetic Retinopathy (DR) is a type of complications caused by diabetes. Patients with DR may experience worsening vision, blindness, and eye pain. To effectively address this disorder, DR must be identified and classified according to its severity. Therefore, automated diagnosis of fundus lesions is of great interest for DR early detection. The development of deep learning technology has provided a strong foundation for effective implementation of the automated detection system. In particular, transfer learning techniques have greatly benefited the research community to reduce computation and reuse trained features. In this paper, the outputs from the ”average pooling” and ”fully connected” layers are used as the features to the Support Vector Machine (SVM) classifier with Error Correction Output Code (ECOC). The proposed method outperforms the fine-tuned pre-trained networks in predicting the severity classes with an accuracy of 80.1%. This means that multiple features extracted from the pre-trained networks contribute to a better recognition process.
Gait Planning for Underactuated Bipedal Robot on Discontinuous Ground Based on Particle Swarm Optimization

This study develops a gait planning method for underactuated bipedal robot on discontinuous ground based on Particle Swarm Optimization (PSO). First, we introduce an underactuated bipedal walking gait planning method based on Center-of-Mass (CoM) motion state. Second, based on human walking characteristics, we propose a gait planning method to realize underactuated bipedal walking on discontinuous ground. We change the step length and the ideal tracking speed of CoM, in order to eliminate the influence of the ground discontinuity on walking stability. By using PSO, we obtain the ideal tracking speed of CoM for different gait. Finally, the effectiveness of the proposed method is validated with a numerical simulation and a physical experiment. The experimental results show that underactuated bipedal walking can be realized on discontinuous ground by using the proposed method to control robot step length and the ideal tracking speed of CoM.
Design and Control of Soft Pneumatic Actuator with Embedded Flexion Sensor

Soft robotics has been widely explored in the past decade due to their ability to adapt to different environments and interact softly with external objects. This paper presents the design, manufacture, model identification and control of a low cost soft robotic pneumatic actuator with sensing capabilities, the proposed actuator has potential application in prosthetic and orthotic devices. Initially, the design and fabrication of the actuator is presented, including the design parameters and manufacturing techniques. The system was modeled using system identification techniques allowing to implement a PI controller to control the actuator deformation. In addition, the design and fabrication of a soft bending sensor, based on a variable resistance material, was presented. The sensor was used as feedback element for the control system and allowed to control the deformation of the actuator. The soft actuator only requires 0.4 bar of pressure to achieve maximum deformation; the controlled actuator achieved a settling time of 4.5 s and an steady state error of 2 %.