Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilient Detection of Cyber Attacks in Industrial Devices

With the advent of smartphones, laptops, and home computers, smart systems are becoming more and more flexible. As the use of internet increases, there will be more cyber threats occurring on most third-party connectivity websites. The powerful technique used to detect the threats present in the IoT applications are discussed in the proposed system. Based on the KAGGLE NIDS(Network Intrusion Detection System)(Intrusion Detection System) dataset, the number of possible attacks is calculated in the proposed architecture. A similar occurrence of intrusion creating a task is detected by the system, triggering the model to prevent the intrusion by notifying the user immediately. The existing attack detection systems have a number of limitations which includes the need of human intervention to detect the attacks encountered, slower detection rate and inaccuracy in detection. An advanced deep learning algorithm is proposed for detecting possible intrusions to overcome these limitations. The proposed design focuses on creating a Novel architecture using Adaptive convolutional neural network for improving the accuracy and significantly raising the detection rate above that of the current approaches there by aiding in the immediate detection of intrusions.
Efficiency Comparison of 2-Level and 3-Level Si IGBT Based Inverters

In this study, an experimental study of a 20 kVA 2-level neutral point inverter and a 3-level diode-clamped inverter has been carried out under 4 different linear loads. At the end of the experimental study, oscilloscope images obtained under 4 different loads were shared. The efficiency of the 2-level neutral point inverter and the 3-level diode clamped inverter under 25%, 50%, 75%, 100% linear loads were measured with a power analyzer. The efficiency comparison results of the 2-Level and 3-Level inverter are shared.
A Study on Gait Pattern Identification and MNA Detection Methodologies used for Human Medical Diagnosis using Wearable Sensors

In ambulatory monitoring, different sensors such as pulse oximetry, accelerometer, gyroscope, etc. are used for monitoring probes. Generally, these sensors are used for monitoring daily physical activity, stress, and sleep pattern of the respective probe. However, the measurement obtained from these sensors is highly affected due to motion and noise artifacts (MNAs). This study conducts a survey of various existing methodologies presented for mitigating motion and noise artifact of measurement obtained through wearable sensors. The survey is composed of both supervised and non-supervised techniques used for mitigating MNAs, detecting HAR, and classifying Obstructive Sleep Apnea (OSA) using wearables. This survey further highlights research issues in monitoring probes using wearable sensors and presents a future solution to overcome research issues.
Location, orientation, and speed tracking of a robot using ROS: a case study with Whycon

In this paper, a system that can track the absolute position, orientation, and speed of a single mobile robot was developed using ROS and the WhyCon localization algorithm. ROS nodes that receive WhyCon coordinates and return the orientation and speed of a mobile robot were developed. On-site experiments verify the accuracy of the system. Percent error of the computed position was found to be 6.67%, with orientation error at 0.612%, and speed error at 3.68%. The system can be used in a mobile robot testing facility.
Comparative Analysis of Different Approaches to the Music Recommendation System

Digital music and music streaming services have led to a significant expansion in the variety of music available. For anyone, sorting through this music would be impossible. Music recommendation systems that use genre, artist, instrument, and user evaluations to automatically suggest music greatly reduce the amount of manual effort required. Although commercial use of music recommendation systems is widespread, there is no perfect system that can offer the customer the greatest music recommendations with the least amount of human effort. This research examined various recommendation systems currently in use, including content-based, collaborative, emotion-based, and other approaches. In addition to discussing the various recommendation methods, this research also explored the advantages and disadvantages of each approach. Finally, an overview of a potential music recommendation system that addresses many of the challenges faced by current hybrid recommendation systems was provided.
Demonstration of Open Radio Access Network Intelligent Controllers

The control of the Open Radio Access Network (RAN) architecture via a RAN Intelligent Controller (RIC) is one of the main innovations in the next-generation mobile communication network. This paper analyzes three of the most significant RIC designs using apps that monitor key performance metrics. In this way, it is possible to compare these designs in terms of functionality, highlighting their potential for practical implementation.
$H_{\infty}$ control against mixed DoS attacks for cyber-physical systems

This article focuses on the $H_{\infty}$ control against mixed denial of service (DoS) attacks for cyber-physical systems (CPSs), where attacks are under zero-input and hold-input strategies. By introducing a unified model describing the simultaneous existence of the two attacks, the CPS can be converted to a switched system with one delay. To ensure control performance, the type-dependent average dwell time (ADT) is applied for the first time to pose constraints on the occurrence frequency of DoS attacks. In the meantime, multiple discontinuous Lyapunov functions (MDLFs) are employed. Upon this, the global uniform exponential stability (GUES) and $H_{\infty}$ performance of the closed-loop system are guaranteed. Finally, the effectiveness of our theoretical results is verified by a numerical example.
Parameter Extraction from a Resistive Load Inverter Circuit Using Supervised Learning Methods

This paper presents a proposal for parameter extraction of a resistive load inverter circuit, with a Thin Film Transistor (TFT), using Artificial Neural Networks, Random Forest, Decision Trees and Support Vector Regression. Although analytical and optimization methods are usually used for this purpose, they have disadvantages such as the need for expertise or complex implementation. This work shows that these supervised learning methods are useful for this task because they can learn the parameters of the device transfer curves, obtaining a good fit between the measurements and the extracted parameters. The different methods were trained using a data set constructed from simulations performed with AIM-Spice software, where the parameters affecting different regions of the inverter characteristic curve were extracted. In the experimental stage, the Neural Networks obtained better results, with an average error rate of 6.04%. The method was also applied to real NMOS measurements and yielded minimum errors of up to 0.43%.
The Detection of Fake News through the use of a Hierarchical Convolutional Attention Network

In the current digital era, the popularity of social media platforms has expanded tremendously. Every one of us, no matter what age or gender or class we belong to, are active on social media, sharing so much of our personal and professional life with the world. We are connected with people, countries and borders apart via our phone now. News companies are also recognizing this change in the world and are now making business through this by publishing information online. The rapid spread of an article on the internet by the normal public without checking for its authenticity is resulting in a surge in the amount of incorrect information available online. No end-user finds it simple to confirm the veracity of a given news article. These fake articles are increasing rapidly and making it far more difficult for the real information to reach the public. The purpose of this work is to develop an efficient model for assessing the credibility of a news article. This work addresses the classification problem of Fake News, given just the news article's content and the author's name. The system proposed in this paper works with Hierarchical Convolutional-Attention Network (HCAN) and Convolutional Neural Network to classify the fake news articles based on the words and phrases used in the published piece. Hierarchical Convolutional-Attention Network (HCAN) is comprised of attention-enhanced word-level and sentence-level encoders as well as a CNN to capture sequential correlation. We have also used Recurrent Neural Network (RNN) for word encoder and sentence encoder in our system. We carried out the experiment on our system using a dataset and compared the results with various baseline models like CNN, RCNN, BERT or LR. Extensive trials demonstrate that HCAN outperforms the most advanced baseline models on a collected dataset. Results of our experiment shows that our model is achieving the F1 score of 0.98.
Strength of Data Matrix Image over Analysis and Design of Exam Processing System

This paper planned a new system for data matrix image based Exam processing System, where the image is treated as authorized user’s unique identity. This system helps to store the user identity number and image to restrict unauthorized access of exam processing system. Further proposed procedure which helps to remove the drawbacks of password authentication and bypass the risk generated from password authentication system. This system stores all information circulated with in a network. Exam processing system restricts unwanted user access a resources. In this paper we proposed a new Data Matrix image password based system techniques that offer benefits over current system.
DC, RF and Noise Characterization of AlGaN/GaN HEMT

In this work, DC and RF characterization of AlGaN/GaN HEMT on SiC substrate was carried out. It was found that the maximum drain on current (I<inf>ON</inf>) was 0.12 A/mm at a drain to source (V<inf>DS</inf>) bias of 9 V and at the gate to source bias (V<inf>GS</inf>) of 2 V. The maximum transconductance (g<inf>m</inf>) of the device was calculated to be 0.02 S. S21 parameter of the device was measured from 100 MHz to 50 GHz. The measurement of S parameters was done by the variation of V<inf>GS</inf> and V<inf>DS</inf>. The Noise Figure measurements of the device were done by varying the frequency from 2 GHz to 10 GHz using a Noise Figure Analyzer.
ADAMAX optimizer and CATEGORICAL CROSSENTROPY loss function-based CNN method for diagnosing Lung cancer

Cancer is a disease with many causes. It is caused by a complicated interaction between genes and the environment. A person's particular genetic makeup may increase his risk of developing cancer. According to World Health Organization statistics, cancer is the top cause of death worldwide. There have been recorded instances of a certain genetic illness. Thus, a proper understanding of eco-genomics is essential in order to assess the underlying cause and risk factors for cancer. Pathology is the core of cancer treatment. Pathology photographs illustrate the histomorphological traits of tumors in detail. To manually identify and classify tumor locations in pathology images, however, requires a great deal of effort and discretion. Using machine learning techniques, one can construct an automated tumour region recognition system for lung cancer pathology images. As customized cancer treatment needs precise biomarker evaluation, the need for precision in histopathologic cancer diagnosis increases. With the help of digital image analysis, histomorphological evaluation could become more accurate and have a wider range of uses. Today, machine learning, especially deep learning, has helped computational pathology grow quickly. Using machine learning in traditional healthcare will be a turning point for the field over the next 10 years, and histopathology will be at the forefront of this change. Lung cancer is one of the leading causes of death for both men and women in every country. Lung cancer has a significant fatality rate owing to its poor prognosis. With image recognition and data analysis, the computer industry and the medical industry are both moving towards full automation. This study wants to find out how accurate the CNN model is so that lung cancer can be found early and maybe many lives can be saved. This study found that the adamax of categorical-cross entropy is about 0.94, which is more accurate than the results from other studies that used different methods.
Adhesive Strength and Diffusion at Interfaces between Sintered Cu Layer and Metal Surfaces (Cu, Ag, Au, Pd, Pt, Ni, NiPx, and NiBx)

Die-bonding technology using a sintering reaction of Cu nanoparticles at low temperatures is an attractive and important joint method for power devices operating at higher temperatures. We report the adhesive strengths of mechanical pressure-assisted sintered Cu (sCu) onto eight different metal surfaces (Cu, Ag, Au, Pd, Pt, Ni, NiPx, and NiBx). The die-shear strengths (τb) of sCu on the metal platings under sintering temperatures (260-300 °C) in N2 were measured. As the sintering temperatures increased, the τb on the Cu, Ag, Ni, NiPx, and NiBx increased, whereas those on the Au, Pd, and Pt decreased. The MD simulations imply that the diffused couples (Au, Pd, Pt) with high a ratio of intrinsic diffusion coefficients can deteriorate the shear strengths and the self- and non-diffusion couples (Cu, Ni, Ag) can keep the high shear strengths.
IOT-based Smart Trash Bin for Real-Time Monitoring and Management of Solid Waste

The significant growth of the Internet is leading to the emergence of new technology, the Internet of Things (IoT). IoT is the backbone of future communication systems where everything will communicate and share information smartly without human instructions. In smart cities, one of the important problems is waste management. Monitoring and disposing of waste are very important to keep the environment green and clean. This can be efficiently achieved by minimizing human involvement and using IoT-based networks. This paper aims to solve the waste management issue using the Smart Waste Mechanism (SWM). Install Smart Bins (SB) in various optimum locations and communicate with the Trash Collecting Vehicles (TCV). Optimize their routes to the designated Smart Bin for collection whenever prompted by the Smart Bin.
Secrecy Outage Analysis of Energy Harvesting Enabled Two User Cooperative NOMA

In this paper, we evaluate the secrecy outage probability (SOP) of two user cooperative non-orthogonal multiple access (C-NOMA) network in the presence of a passive eavesdropper (E). The near user <tex>$(U_{1})$</tex> harvests energy from the superimposed signal transmitted by source (S). The near user <tex>$(U_{1})$</tex> acts as a relay and retransmits the message signal to a far user <tex>$(U_{2})$</tex>. The E eavesdrops the signal for <tex>$U_{1}$</tex> and <tex>$U_{2}$</tex> transmitted from <tex>$\mathbf{S}$</tex> and eavesdorp the signal of <tex>$U_{2}$</tex> relayed from <tex>$U_{1}$</tex>. An MRC is applied at E on two copies of signals obtained for <tex>$U_{2}$</tex>. An analytical framework for evaluating SOP is presented. We obtain an analytical expression for SOP of <tex>$U_{1}$</tex>, while an analytical expression of SOP of <tex>$U_{2}$</tex> at high SNR approximation is obtained. Impact of several netwrok parameters such as target secrecy rate, energy harvesting parameters on SOP is indicated. Our analytical results are supported by monte-carlo simulation carried out in MATLAB.
A Semantic Approach for Automated Hiring using Artificial Intelligence & Computer Vision

This research paper presents an automated interview system that utilizes natural language processing and deep learning techniques to conduct job interviews. The system comprises 4 modules, the first one being resume classification system, which is built on state-of-the-art NLP architecture and transformer-based BERT model. The model uses Named Entity Recognition and Text Classification to extract relevant information from the resume. The resume classification algorithm is based on the principle of context relevance, and skill mentions in the resumes are validated through an analysis of the candidate's experience and completed projects before being classified. The subsequent module utilizes the output from the resume model to generate a set of relevant interview questions for the candidate through utilizing natural language processing techniques and semantic analysis. The difficulty of the following questions is adjusted owing to the interviewee’s answer, very much like what human HRs do. Such questions are extracted with the help of web scraping by selenium driver. One of the most innovative parts of this system is its ability to reduce bias in the hiring process by using machine learning models to evaluate candidate responses. The following module assesses the candidate's personality during the interview using the CNN and LSTM models. In addition to generating relevant interview questions, the system also detects potential instances of misconduct or malpractice during the interview process using Open CV’S DNN module and a quantized model. Overall, this system has the potential to revolutionize the way companies conduct job interviews and improve the hiring process.
Vehicle Lane Detection for Accident Prevention and Smart Autodrive Using OpenCV

Road accidents have been rising alarmingly, and the reason for this increment is because of drivers being distracted. As a result, several technical advancements have lately been developed in the field of road safety. There are many systems available that can alert drivers to various threats, including lane departure, the potential for collisions, and numerous traffic signs. Through the use of lane detection systems, which essentially assist by identifying the lane borders of the road and then alert the driver if he moves to an improper lane, it is one way to go about this, moreover during the night times where chances of less visibility and losing concentration by driver can occur would also be looked over. So the use of multiple techniques can be enhanced by upscaling the images. The use of gamma correction is going to help to increase contrast and upscale the images for better recognition of the lanes at night which are dark and are hard to analyze due to less light.
Shopify - Shop Augmentation and Recommendation System

Modern times have brought about changes in lifestyle. People dislike visiting crowded markets because they take too long and are uncomfortable. Thus, an advantage of online shopping is, it saves tons of time. Online purchasing is a procedure where clients go directly to a vendor on the Internet to buy products, services, etc. Customers can browse online merchants from the convenience of their home and make purchases while seated at a computer. This research aims to introduce local people to the online shopping application so that local merchants can profit from it and expand their reach and sales through this fantastic platform. This research aims to give clients of physical stores the benefits of internet shopping. It facilitates purchasing goods from any store using a smart device and the internet. In this way, the customer will benefit from home delivery and online shopping services from his preferred retailer. The solution is different in an umpteen number of ways. It is a direct exchange between the buyer and the vendor. This system can be applied to any local business or to international brands with retail store chains in the local areas.
Big Data Analytics Cloud based Smart IoT Healthcare Network

A smart healthcare network combines the advantages of sensing devices, the Internet of Things (IoT), and big data analytics to give better patient comfort and lower healthcare costs. The healthcare sector is currently faced with enormous difficulties in storing and processing the data that is generated in order to derive information from it. It is vital to create new techniques and strategies for processing the growing amount of health sector data produced by IoT devices, e-health, m-health, and telemedicine airing. The physical intricacy of medicinal data, the demand for remedial competence as well as the protection of individual medicinal data distinguish research of data mining in health sector from various data studies. This research addresses these difficulties by presenting a contemporary technique for IoT based big data execution in healthcare. a large data processing and IoT health stage platform on the cloud for healthcare system has been introduced that lowers medical data management expenses while improving safety. Remote monitoring, the use of clever algorithms, tools, and processes, quicker analysis, and the engagement of experts for better treatment references, this learning aims to deliver health sector facilities to both the ill and the healthy community. With the integration of technologies, healthcare service delivery has evolved significantly. This study suggests a brand-new big data smart healthcare framework for remotely monitoring people's everyday physical activity, both healthy and sick.
How rotation breaks the Hamiltonian character of rough-hard-disk systems

The Oseledec splitting of the tangent space into covariant subspaces for a hyperbolic dynamical system is numerically accessible by computing the full set of covariant Lyapunov vectors. In this paper, the covariant Lyapunov vectors, the Gram-Schmidt vectors, as well as the corresponding local (time-dependent) Lyapunov exponents, are analyzed for a planar system of rough hard disks (RHDS). These results are compared to respective results for a smooth-hard-disk systems (SHDS). We establish that the rotation of the disks deeply aects the Oseledec splitting and the structure of the tangent space. For the rough hard disks, the localization of conjugate covariant vectors in physical space agrees, whereas it does not for conjugate Gram- Schmidt vectors. For both the smooth and rough hard disks, the stable, unstable and central manifolds are transverse to each other, although the minimal angle between the unstable and unstable manifolds of the RHDS typically is very small. Both systems are hyperbolic. However, the central manifold is precisely orthogonal to the rest of the tangent space only for the smooth-particle case and not for the rough disks. We also demonstrate that the rotations destroy the Hamiltonian character for the rough-hard- disk system.
Hybrid Machine Learning Model for Lie-Detection

A technique for recognizing a person from his photograph is facial recognition. Due to its extensive range of applications in several fields, it has drawn the attention of numerous researchers in the field of computer vision in recent years (Cyber security, crime cases, and biometrics). This technology's operation is based on the extraction of features from an input picture using methods like PCA, ICA, LDA etc. After comparing them with others from another image to verify or assert an individual's identification. Via this work, we applied amalgamation of CNN and SVM techniques to two face datasets that will be split into two groups in a machine learning-based methodology. We assessed different machine learning-based lie detectors using our amassed dataset. Our findings demonstrate that combined CNN with SVM task achieved accuracy up to 58%.