Miracle-dz/UniFAR-FTUs huggingface dataset:
Accident Detection Using Fog Computing

Emergencies, in terms of definition, are the irregular and contiguous answer is a necessary demand for emergency administration. Worldwide, an important number of deaths arise each year, resulting in necessary delays in saving operations. To minimize these delays and save the lives of people when have come up with a project that is based on the computer network that uses Fog computing. It contains three modules in which collision detection is to be carried out. Pre-recorded videos and live videos have been broken down into frames and sent to the MQTT broker which is a lightweight network protocol to send and receive messages. Video Frame is sent to Processing Unit for detection. Results are sent back to MQTT Broker and subscribed devices are informed accordingly. In this Live testing is being done on highways, apart from this Training of the model on various constraints using a mask and fast R-CNN is being done followed by a validation phase that shows the efficiency of this project. The result shows the need to save the lives which are in danger due to accidents by minimizing the time to contact the required department. This delay is minimized with the help of a concept called Fog Computing which serves as a middleware between devices and the cloud therefore saving time.
Comparative Analysis of Tomato Leaf Disease Detection Using Machine Learning

Disease transmission from affected crops to unaffected crops is getting the most dangerous things to agriculture. Epidemics spread like wildfire can affect entire farms if not spotted early. The plant disease detection method helps to identify infected plants at a very early stage and also helps users to extend plant disease identification for a variety of plants in an efficient manner. The purpose for work is to test the data using different machine learning models, Convolutional Neural Networks (CNN), K Nearest Neighbors (KNN), Visual Geometry Group (VGG-19), and apply them to plant disease detection on tomato leaves. The results on the implementation of machine learning models show that the KNN model performs better on every evaluation indicator.
Attentional Deep Learning novel approach for Facial Expression Recognition

The use of intelligent human-computer interaction, which recognises human emotion based on facial expressions, is significant and has a big impact on people’s daily lives and work. One of the most important applications of computer vision is the recognition of facial expressions. However, there are certain issues with the present research on facial emotion identification, such as the network model’s limited capacity for generalisation and the recognition system’s weak level of robustness. In this article, we provide a method for feature extraction that blends convolutional neural networks for facial emotion identification attentional deep networks. It can be demonstrated that this network performs better at emotion recognition than the current mainstream facial emotion detection models by the experimental simulation of the most challenging data set FER-2013.
A Novel Deep Learning Framework with Multi-Stage Feature Selection for Time Series Forecasting

Time series prediction across different sectors has received enormous consideration as it influences intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied around modelling and forecasting multivariate time series data. Due to the intricate nonlinear patterns and significant variations in the randomness of different categories of real-world time series data, it is a formidable task for a deep learning model to simultaneously achieve effectiveness and robustness. We proposed a model to counter the challenges and provide more precise results as compared to existing state-of-art models. Firstly, a feature selection is performed via two strategies concurrently. Subsequently, the data is converted into an image, and then Convolutional Neural Network (CNN) is used to extract attributes from the images. Concurrently, the clustering is done on the data, and each cluster is fed to the residual Long Short-Term Memory (LSTM) so that the lower layers can transmit information directly to the upper layers through highways, increasing information flow freedom. Because of this, gradients can be passed more directly through the layers with the addition operators making training much easier. Then the output of CNN and Residual LSTM is combined and passed to the machine learning models. The model is benchmarked on a couple of datasets. For the capital bike sharing dataset, the proposed model improves the Root Mean Squared Error by ~15% and Mean Absolute Error by ~20%.
Evolution and Recent Trends in Detecting Driver Drowsiness

Road accidents are the leading causes of mortality in the contemporary period globally. Drowsiness while driving is one of the primary causes of collisions that result in critical injuries, premature death, and considerable financial losses. There are several sleepiness detection techniques that track drivers' drowsiness levels while they are on the road and alert them if they are not paying attention. The purpose of the research is to understand the various methodologies being used to identify driver inattention at early stages and contribute to the prevention of accidents. It also explores the most recent techniques and technologies being used to combat driver drowsiness. Various features of a human being such as the head, mouth, and eyes to detect the early stages of drowsiness are used because they are less intrusive and more accurate. Further, the study depicts how these technologies are being evolved to utilize basic techniques such as vehicles or subjective or physiological measures to bring out a more robust and confirmed state of early drowsiness. The study frameworks are also explained in diagrammatic and tabular versions for easier comprehension. In the methodology, survey-based study findings are derived from the technique, which can help new researchers discover potential future work on the subject. Finally, the main contribution of the article is to investigate, identify, and choose the most effective measurement combinations to detect driver fatigue. In addition, it also compares various techniques from traditional to hybrid, their performances, and their limitations.
Design and Implementation of Smart Multipurpose Robot using Vex and Node MCU

The main objective of this research is to design and implement a Node Micro controller Unit (MCU) based multipurpose innovative robot which is capable of washing and painting the exterior and interior walls of high-rise buildings, watering plants and other tasks. Robots have grown to be crucial in modern society, due to their ability to complete tasks faster than humans. In this project work, a Vex cortex microcontroller is utilized to operate the robot. The most significant risks associated with cleaning and painting interior and external walls include breathing in paint fumes, causes issues with their eyes and respiratory systems. These reasons have led to the development of multipurpose robot that can wash and paint tall building walls, and irrigate farms professionally and safely in a short amount of time. There is also a servo motor that controls the lever that does the painting, and an ultrasonic sensor that detects the robot’s distance from the wall to be painted or the plant to be watered. The designed robot has the ability to paint an area of 0.302 m2 in 28.6 minutes which has an accuracy of 95.4%. It also has a water level and battery monitoring sensor that measures water or color and battery status & sends message to the user via Thing Speak. The designed multipurpose robot exhibits excellent precision performance, effective, simple and easy to implement.
Early Detection of Melanoma Using Convolutional Neural Network and Random Forest Algorithm

The detection melanoma is so difficult to identify and is the most severe of all skin diseases, skin cancer. When all other types of skin cancer are compared side by side, melanoma is considered the most deadly and responsible for most skin cancer fatalities. It originates from the skin cells called melanocytes, which produce the pigment melanin. Most melanomas are black and brown. Computer-assisted technology may detect melanoma by classifying dermoscopic images of melanoma and non-melanoma based on the training set and the added weights to the system, which is based on the Machine learning approach. The automated diagnosis makes it difficult to make an early diagnosis of melanoma. The Deep feedforward Neural Network algorithm's architecture will be changed by utilizing the automatic diagnosis using deep learning, a subsection of machine learning, making early detection feasible. The neural network's layers are cut down to eight, which shortens the detection time and allows for early melanoma diagnosis. The architecture, which derives its name from the Visual Geometry Group, is known as octal architecture (VGG -16). The distinct characteristics of the incoming data will be extracted using the layers of the architecture and used for categorization. More than 1697 photos were employed in the experiment's training set, which led to early detection with 96% accuracy. For training the random forest model 510 images were taken. Testing is also performed with a random forest algorithm and based on accuracy the efficient method is chosen. The application is created using the Python programming language.
Dysphonia-based Parkinson's Detection using Deep Learning and Ensemble Techniques

Parkinson’s disease (PD) is the second most prevalent neuro-degenerative disorder which affects the central nervous system causing the death of certain neurons in the nerves, which leads to a permanent loss of motor functionality and decreased production of Dopamine. Parkinson’s disease has a wide range of symptoms which affect both the motor and non-motor functionalities of the brain, making it extremely difficult to detect in the early stages because of its common symptoms which are shared with other medical conditions. In this study we focus on detecting the Parkinson’s disease using the phonetic features of a patient which include Shimmer, Jitter, NHR among others. This study is conducted to analyze the performance of various machine learning, deep learning and ensemble techniques and optimize their hyperparameters. Further, we use these hyperparameter-tuned models to create and analyze various stacking models which detect the disease more effectively.
An Integrated Approach of Fire Identification utilizing Web of Things and Aerial Robots

Savvy cities are a booming field of research today. Agricultural lands, forests, commercial and residential areas can have fire occurrences that lead to loss of life and resources. In this paper, remote sensor organizations and automated aerial vehicle advancements are employed for identification of fire at a beginning phase. These can help in staying away from serious fire misfortunes. The ecological parameters are checked here utilizing sensor design. Sensors involve WoT based applications for handling assembled natural information. To work on the exactness of the framework, a mix of picture handling scheme is used. The framework discussed here empowers checking and assortment of constant data in an expense proficient way. Incorporation of picture handling plan further improves proficiency.
Analysis of the Requirement and Architecture for the Internet of Drones

Internet of Drones (IoDs) is a network meant to connect drones and their users through the Internet. It is now possible for industries to perform a variety of missions in a controlled airspace utilising several drones that are easily accessible as commodity products. Unmanned Aerial Vehicles (UAV) components, such as Central Processing Units (CPUs), sensors, storage, and battery life, may be mass-produced thanks to technological advancements, but the performance constraints of these components obstruct and restrict expectations. In order to provide remote control and accessibility of UAV and efficiently scalable unloading and probabilities of remote storage of data in the cloud system, IoDs deliver drones coupling vehicle and cloud mobility features. In this article, comprehensive overview of the Internet of Things (IoTs), requirements of IoDs and its architecture is reviewed. Drone integration with the IoTs has recently advanced, and this bodes well for worldwide growth.
Artificial Intelligence based Model Using Various Machine Learning Techniques to detect DDoS Attack

The magnanimous increase in IoT devices has led to the attention of hackers and malicious user at a massive rate, with the increase in devices and their applications the attacks on the devices are also increasing. These attacks aim to disrupt the quality of services and can be fatal sometimes, the common attack of all is the Denial-of-service (DoS) attack. DoS attack targets the device by exhausting the system by using its all resources and therefore denying the service to the end-users. This paper discusses both Unsupervised and Deep Learning (DL) techniques to detect DoS attacks in IoT devices. We have used Knowledge Discovery in Databases (KDD) unlabelled datasets to achieve our goal, clustering, and deep auto-encoding techniques were used for classifying and training the data, and finally Supervised learning was used for the evaluation of the classifiers in detecting the DoS attacks. The finding from the studies claims that most of the classifiers seem to be performing well in detecting the DoS attack except Naive Bayes which seems to be performing on average, also the various clustering techniques provides encouraging results except the Gaussian mixture model which seems to be performing average in all the cases as compared to other clustering techniques.
Computational Intelligent Techniques and Analysis on Automated Retinal Layer Segmentation and Detection of Retinal Diseases

The number of critically sick patients is increasing, placing pressure on the healthcare system, which is witnessing twice as many new cases as a year ago. Artificial intelligence (AI) technologies are advantageous to the health business because they offer better and more accurate autonomous sickness detection. Artificial intelligence applications are revolutionizing image classification and pattern recognition, two fields of commerce. Optical coherence tomography, or OCT, is a noninvasive imaging method that provides high resolution without causing pain to the patient. It is used to study the retina’s anatomy. Non-invasive images of a cross-sectional volume of tissues may be obtained using OCT. Picture segmentation techniques and structure classification strategies that have been found to be the most successful among the presently available research are discussed in this review study. This study presents a short overview of all of the most recent ways for applying machine learning algorithms to the problem of predicting retinal abnormalities based on OCT images. Readers will be able to find the algorithm that delivers the best degree of accuracy for retinal classification of infected and healthy eyes while also taking the least amount of processing time for layer segmentation using the techniques mentioned in this research study. As a consequence, accuracy improves while the amount of time needed decreases.
An efficient Heart-Disease Prediction System using Machine Learning and Deep Learning Techniques

One of the leading causes of death globally is stroke and it is highly essential to develop solutions to identify stroke. Though early detection is helpful in saving human lives, hence it is required to collect many physiological data such as blood pressure, ECG, glucose level and cholesterol level using wearable devices. Fog computing assisted processing is initially used to store and analyse large amounts of data. But in this approach, initial quality assessment, many preprocessing tasks, and feature extraction are the challenges in the fog computing layer, and the decisions are obtained at increased computation time. The main objective of this paper is to develop efficient disease prediction systems capable of accurately predicting stoke and heart related diseases, as well as determining the severity level of the diseases with less computation time. Initially we use Fog computing framework for classifying the parameters an individual patient. An Enhanced Grey-Wolf Optimization-based Feature Selection Algorithm (EGWO-FSA) and Deep Belief Network (DBN) disease prediction system is used to predict heart, stroke and diseases. According to the proposed approach, quantitative analysis is done to determine parameters like specificity, accuracy, as well as sensitivity.
Making Spatial Analysis Operational: Commands for Generating Spatial Effect Variables in Monadic and Dyadic Data

Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent, or actor, and even more true for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents, or actors. Dyadic data offer more complex ways of modeling spatial-effect variables than do monadic data. The commands described in this article facilitate spatial analysis by providing an easy tool for generating, with one command line, spatial-effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.
AN EMPIRICAL STUDY OF THE SUITABILITY OF CLASS DECOMPOSITION FOR LINEAR CLASSIFIERS

The presence of sub-classes within a data sample suggests a class decomposition approach to classification, where each subclass is treated as a new class. Class decomposition can be effected using multiple linear classifiers in an attempt to outperform a single global linear classifier; the goal is to gain in model complexity while keeping error variance low. In this dissertation, we propose a study aimed at understanding the conditions behind the success or failure of class decomposition when combined with linear classifiers. We identify two relevant data properties as indicators of the suitability of class decomposition: 1) linear separability; and 2) class overlap. We use well-known data complexity measures to evaluate the presence of these properties in a data sample. Our methodology indicates when to avoid performing class decomposition based on such data properties. In addition we conduct a similar analysis at a more granular level for data samples marked as suitable for class decomposition. This extra analysis shows how to improve in efficiency during class decomposition. From an empirical standpoint, we test our technique on several real-world classification problems; results validate our methodology.
Exact support recovery via orthogonal matching pursuit from noisy measurements

The support recovery performance of orthogonal matching pursuit (OMP) for the l ∞ bounded noise is presented. It is shown that OMP can exactly recover the support of arbitrary K-sparse signal x from noisy measurements y = Φ x + e in K iterations, provided that the matrix Φ and the minimum magnitude of all the non-zero elements of x satisfy certain requirements. The proposed result is an improvement over the existing ones.
Joint Decision-Making and Team Outcomes: Examining Cross-Lagged Relationships and the Roles of Psychological Safety and Participative Leadership

ABSTRACT Despite increasing studies on team creativity, the relationship between joint decision-making and team creativity needs further exploration. Our study employs social exchange theory to investigate the potential bidirectional association between joint decision-making and team psychological safety while also exploring the role of participative leadership as a contextual constraint on this relationship. Through our analysis, we aim to shed light on the vital role of team psychological safety in mediating the moderating effect of participative leadership on the linkage between joint decision-making and team creativity. We test our hypotheses using a sample of 372 employees in 75 teams with a time-lagged research design. Our results demonstrated that joint decision-making is positively related to team psychological safety, and participative leadership strengthened the association between joint decision-making and team psychological safety and the association between joint decision-making and team creativity via team psychological safety. Overall, our findings suggest that team creativity could be improved if leaders can enhance their participative behaviors and identify appropriate channels for employees to feel psychologically safe in the workplace.
The Love surface-acoustic wave seen as a guided non-Newtonian quasi-particle

Following along the line of recent works, the paper introduces the notion of quasi-particles that are associated with surface-acoustic waves of the Love type via the canonical conservation laws of Noether’s theory of invariants. The strange point mechanics (velocity-dependent “mass” and energy) is obtained by integrating the conservation laws of “wave momentum” and energy over an element of volume representative of the wave motion. This original but involved point mechanics obtained for the quasi-particle is neither Newtonian nor Lorentzian because of the existence of surface waves restricted to a bounded interval of velocities that is characteristic of Love waves. The reduction of this description to the cases of an isolated elastic layer of finite thickness with pure shear horizontal motion and of the so-called Murdoch case corresponding to a thin-film approximation (half-space with an upper material boundary equipped with mass and surface elasticity) studied independently is exactly proved.
Quantum chaos in quantum dots coupled to bosons

Chaos transition, as an important topic, has become an active research subject in non-linear science. By considering a Dicke Hamiltonian coupled to a bath of harmonic oscillator, we have been able to introduce a logistic map with quantum corrections. Some basic dynamical properties, such as Lyapunov exponents and bifurcation diagram of the model are studied. we show that in this model, the transition from integrable motion to periodic, chaotic and hyperchaotic as the control parameter $r$ is increased.
A Compact Triple-Band and Dual-Sense Circularly Polarized Truncated Patch Antenna

A triple-band dual-sense circularly polarized truncated patch antenna is proposed. The truncated patch is fed by the aperture etched on the ground plane. Firstly, a <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-axis directional rectangular slot is introduced in the truncated patch to excite two orthogonal modes for left hand circular polarization(LHCP). Then an <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-axis directional rectangular slot is employed to obtain right hand circular polarization (RHCP) performance. After that, one parasitic stub is loaded in the central of the <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-axis directional rectangular slot, thus, an extra RHCP can be realized. Finally, the other parasitic stub is loaded beneath the truncated patch to improve the impedance and axial ratio performances. The etched slots and loaded stubs technologies are used in a single radiating patch to realize different senses at three different operating bands. And the senses and operating bands can be controlled by tuning according slots and stubs. Consequently, a compact triple-band dual-sense circularly polarized antenna is successfully designed. The 3-dB axial-ratio bandwidths of the proposed antenna are 2.6% (3.55-3.64 GHz), 0.24% (4.175-4.185 GHz), and 0.47% (4.26-4.28 GHz), respectively.