Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-Based Zero-Day Malware Detection in IoMT Devices: A Hybrid AI-Enabled Method

Healthcare systems have recently utilized the Internet of Medical Things (IoMT) to assist intelligent data collection and decision-making. However, the volume of malicious threats, particularly new variants of malware attacks to the connected medical devices and their connected system, has risen significantly in recent years, which poses a critical threat to patients’ confidential data and the safety of the healthcare systems. To address the high complexity of conventional software-based detection techniques, Hardware-supported Malware Detection (HMD) has proved to be efficient for detecting malware at the processors’ micro-architecture level with the aid of Machine Learning (ML) techniques applied to Hardware Performance Counter (HPC) data. In this work, we examine the suitability of various standard ML classifiers for zero-day malware detection on new data streams in the real-world operation of IoMT devices and demonstrate that such methods are not capable of detecting unknown malware signatures with a high detection rate. In response, we propose a hybrid and adaptive image-based framework based on Deep Learning and Deep Reinforcement Learning (DRL) for online hardware-assisted zero-day malware detection in IoMT devices. Our proposed method dynamically selects the best DNN-based malware detector at run-time customized for each device from a pool of highly efficient models continuously trained on all stream data. It first converts tabular hardware-based data (HPC events) into small-size images and then leverages a transfer learning technique to retrain and enhance the Deep Neural Network (DNN) based model’s performance for unknown malware detection. Multiple DNN models are trained on various stream data continuously to form an inclusive model pool. Next, a DRL-based agent constructed with two Multi-Layer Perceptrons (MLPs) is trained (one acts as an Actor and another acts as a Critic) to align the decision of selecting the most optimal DNN model for highly accurate zero-day malware detection at run-time using a limited number of hardware events. The experimental results demonstrate that our proposed AI-enabled method achieves 99% detection rate in both F1-score and AUC, with only 0.01% false positive rate and 1% false negative rate.
An Optimized Deep Learning Model for Grassy Shoot Disease Prediction in Sugarcane

Throughout an August-September 20222 study of sugarcane fields at the Sugarcane Research Institute (SRI) in Shahjahanpur, UP, India, a 6% to 28% prevalence of sugarcane grassy shoot disease was reported in several sugarcane varieties. Traditional approaches for disease prediction in sugarcane crops include drawbacks such as high expenses for obtaining the data input required to run the algorithm, a shortage of geographic information, or an inadequate dataset. In this paper, we combine the convolutional neural networks (CNN) model for Grassy shoot sickness prediction and classification, as well as explore the sugarcane issue. This paper investigates the sugarcane grassy shoot infection. The diseased plant is imaged utilizing secondary resources first. The greatest aspects of CNN's deep learning algorithms are utilized to extract and classify the illness's attributes. The study had a 96% prediction rate for Grassy shoot Sugarcane Disease.
Edge-enabled Zero Trust Architecture for ICPS with Spatial and Temporal Granularity

Motivated by the rapid advancement of industrial cyber-physical systems (ICPS) and rising voices in favor of zero trust (ZT) security, in this paper, we present an edge-enabled zero trust architecture (ZTA) for ICPS. ZT is thought to be relevant to ICPS with the spatial and temporal granularity in the suggested architecture. In addition to continuous authentication and a dynamic access-control mechanism at the temporal granularity, we recommend edge segmentation at the spatial granularity with microservices as the division units. Finally, we conduct a security assessment of the proposed architecture in the presence of threats faced by ICPS. Overall, our analysis shows that the proposed ZTA helps to promote the security of ICPS.
Performance Analysis of an Adaptive MPPT Control for a Grid-connected PV Solar System

This paper presents an adaptive maximum power point tracking (MPPT) control of a grid-connected photovoltaic (PV) energy conversion system utilizing neuro-fuzzy (NF) technique. The particle swarm optimization algorithm is used to train the membership functions while the recursive least squares algorithm is used to update the consequent parameters of the NF based MPPT scheme to cope with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck-boost converter to change the output voltage of the solar panel and hence, achieving the maximum panel output power for a given set of environmental conditions. The training data for NF scheme is obtained by operating the system using the perturb and observe (PO) MPPT algorithm. The performance of the proposed NF-based MPPT algorithm is validated in both simulation and real-time. The prototype PV system is built and the designed NF-based MPPT algorithm is implemented in laboratory environment using the DSP board DS1104. It is found that the proposed NF-based MPPT scheme achieves a very fast response with minor oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional PO based MPPT scheme.
Quadcopter Navigation using Telemetry Data

In this project, we demonstrate the usage of telemetry data implied on a Quadcopter. A Quadcopter (also known as Drone or UAV) is controlled automatically by an embedded system known as the “Flight Control System. ” Sensors, GPS and a telemetry system are coupled with an airframe and computer system in this scenario. These components must perform autonomously in various missions and situations while being supervised by a ground base station or controller that serves as a remote transmitter. UAVs can also be effectively employed in so-called “D-cube” missions, which are more explicitly characterized as Dangerous, Dirty, or Dull.
Focusing on the Key Suspicious Trojan Nets with a Collaborative Approach

While most gate-level hardware Trojan detection techniques strive to detect as many as possible suspicious nets, this paper suggests another direction: identifying only a few suspicious nets, in order to reduce the subsequent manual investigation effort, since there is no need to trace multiple suspicious nets that lead to the same Trojan module. To accomplish this goal, we adopt a collaborative approach by a combination of structural-based analysis, testability-based analysis, and behavioral-based analysis to minimize the number of suspicious Trojan nets. Extensive experiments are conducted with Trust-HUB benchmark and an industrial processor. The results are very significant: (1) high precision 95.39%, most of identified nets being actual Trojan nets; (2) high true negative rate 99.99%, most normal nets being correctly identified as non-suspicious; (3) 44% less suspicious nets to greatly reduce the subsequent manual investigation effort; while (4) leading to detect 100% of the Trojan modules.
Severity Detection of Problematic Smartphone Usage (PSU) and its Effect on Human Lifestyle using Machine Learning

Although smartphone addiction has recently become a concern about that based on the usage habits of individual smartphone users, it can be challenging to anticipate the number of problem smartphone users. Smartphones are increasingly ingrained in our daily lives as a result of their rapid uptake. This study aimed to find the possibility of predicting smartphone addiction levels based on their use of smartphones. A survey was created to collect the raw data from people 15 to 25 years old. The data were examined using Machine learning techniques(SVM, Logistic Regression, KNN, Decision tree, Random forest, AdaBoost, XGBoost, LightGBM) in addition to hypothesis tests, integrating fundamental personal traits and smartphone usage data. There were 23 different factors used to predict the extent of smartphone addiction, and the SVM(0.82) model had the best accuracy rate while the Decision tree had the lowest (0.60) with the feature chi-square. And without the feature, the highest accuracy rate was 0.76 in the logistic regression model. Other models almost remain the same. The result showed that group gender had little bearing on how dependent they were on their smartphones. For future studies, we will try to examine how real-time mood swings vary with smartphone usage and more detailed uses of smartphones to get more accuracy.
Essays on bounding stochastic programming problems

Many planning problems involve choosing a set of optimal decisions for a system in the face of uncertainty of elements that may play a central role in the way the system is analyzed and operated. During the past decade, there has been a renewed interest in the modelling, analysis, and solution of such problems due to a remarkable development of both new theoretical results and novel computational techniques in stochastic optimization. A prominent approach is to develop upper and lower bounding approximations to the problem along with procedures to sharpen bounds until an acceptable tolerance is satisfied. The contributions of this dissertation are concerned with the latter approach. The thesis first studies the stochastic linear programming problem with randomness in both the objective coefficients and the constraints. A convex concave saddle property of the value function is utilized to derive new bounding techniques which generalize previously known results. These approximations require discretizing bounded domains of the random variables in such a way that tight upper and lower bounds result. Such techniques will prove attractive with the recent advances in large scale linear programming. The above results are also extended to obtain new upper and lower bounds when the domains of random variables are unbounded. While these bounds are tight, the approximating models are large-scale deterministic linear programs. In particular, with a proposed order-cone decomposition for the domains, these linear programs are well-structured, thus enabling one to use efficient techniques for solution, such as parallel computation. The thesis next considers convex stochastic programs. Using aggregation concepts from the deterministic literature, new bounds are developed for the problem which are computable using standard convex programming algorithms. Finally, the discussion is focused on a stochastic convex program arising in a certain resource allocation problem. Exploiting the problem structure, bounds are developed via the Karush-Kuhn-Tucker conditions. Rather than discretizing domains, these approximations advocate replacing difficult multidimensional integrals by a series of simple univariate integrals. Such practice allows one to preserve differentiability properties so that smooth convex programming methods can be applied for solution.
MROGI and MPS-P&O MPPT Technique Based Self Reliant SEC-BES System

This paper presents a three-phase, four-wire, grid-integrated, solar photovoltaic energy management system with battery backup. The solar energy conversion-battery energy storage (SEC-BES) system is compatible both in grid-connected mode (GCM) and islanded mode (IM), respectively. The system is self compatible to seamlessly transit between the two modes. In GCM, the system operates using grid-following control as a controlled current source, while in IM, the system operates using grid-forming control as a controlled voltage source. Modified reduced order generalised integrator (MROGI) is incorporated into the control strategy which enables the voltage source converter (VSC) to also operate as a static compensator. As a result, power quality issues at the grid interface get resolved. In grid forming strategy, the VSC adopts a multi-loop control strategy so that a proper sinusoidal supply is available across critical loads. Multiple-power-sampling based perturb and observe maximum power point tracking algorithm has been adopted in this investigation which is compatible especially with slow/rapidly changing environmental conditions. In this paper, seamless transition between IM and GCM is realised in accordance with IEEE-929 and IEEE-1547-2018 by estimating the point of common coupling (PCC) voltage, frequency and phase angle difference range respectively. These parameters get estimated using phase-locked loop strategy based on MROGI. The entire system is realised in a MATLAB environment in a discrete time frame under various operating conditions. The total harmonic distortion of grid current (GCM) and PCC voltage (IM) is observed within 5% in accordance with IEEE-519, 929 and 1547–2018 respectively.
CV-CXR: A Method for Classification and Visualisation of Covid-19 virus using CNN and Heatmap*

Nowadays Covid-19 is prevailing across the world, it has affected millions of populations across the world. The exponential increase in covid cases makes the health care system overwhelmed. Many testing methods are used for covid-19 detection like Rapid antigen test, RT-PCR test, etc. These tests have certain limitations, sometimes people got confused between respiratory infection and covid-19infection, as many symptoms are similar. So for confirming the disease, a chest x-ray is preferred. Covid-19 has similar symptoms of pneumonia, consolidation, and ground-glass opacities, in our approach we consider them as covid. In this paper, images are acquired from reputed hospitals and various online datasets used in Covidnet architecture. After accumulation, the dataset is verified by experienced radiologists. In our approach, we trained our models on various symptoms of covid19 like pneumonia, consolidation, ggopacities and finally on covid-19 dataset images. In our research, we have used single as well as ensemble models for classification. Models like densenet, efficient net, resnet, etc are used. Certain preprocessing techniques are used before passing the image dataset into training like adaptive histogram equalization, data augmentation methods, etc. Finally, a approach based on Deep Learning used for identification of covid 19. We are claiming 95% plus testing accuracy and 99% training accuracy. Beyond classification, we further generate the reports and localize the covid virus on Xray using various visualization methods. Further results are classified based on single and ensemble models on the in-house dataset.
Local Aggregation of PMU Measurements in a Low-Inertia Distribution Power System

The ever-increasing installation of renewable energy sources is rapidly transforming the aspect of modern power grids, especially at distribution level where the presence of photovoltaic power plants and battery storage systems is significant. Phasor Measurement Units (PMUs) are well-known devices that are proven to be particularly effective in this context. However, it is more and more essential to assess the reliability of these measurements, especially in low-inertia and transient conditions. Therefore, this paper presents a study on the feasibility of aggregating PMU-based measurements performed in different nodes of a distribution grid. The results indicate the significance of evaluating such quantity for correct control at a local level, mainly where an extensive installation of renewables is present.
Projection Methods in Sparse and Low Rank Feasibility

In this thesis, we give an analysis of fixed point algorithms involving projections onto closed, not necessarily convex, subsets of finite dimensional vector spaces. These methods are used in applications such as imaging science, signal processing, and inverse problems. The tools used in the analysis place this work at the intersection of optimization and variational analysis. Based on the underlying optimization problems, this work is devided into two main parts. The first one is the compressed sensing problem. Because the problem is NP-hard, we relax it to a feasibility problem with two sets, namely, the set of vectors with at most s nonzero entries and, for a linear mapping M the affine subspace B of vectors satisfying Mx=p for p given. This problem will be referred to as the sparse-affine-feasibility problem. For the Douglas-Rachford algorithm, we give the proof of linear convergence to a fixed point in the case of a feasibility problem of two affine subspaces. It allows us to conclude a result of local linear convergence of the Douglas-Rachford algorithm in the sparse affine feasibility problem. Proceeding, we name sufficient conditions for the alternating projections algorithm to converge to the intersection of an affine subspace with lower level sets of point symmetric, lower semicontinuous, subadditive functions. This implies convergence of alternating projections to a solution of the sparse affine feasibility problem. Together with a result of local linear convergence of the alternating projections algorithm, this allows us to deduce linear convergence after finitely many steps for any initial point of a sequence of points generated by the alternating projections algorithm. The second part of this dissertation deals with the minimization of the rank of matrices satisfying a set of linear equations. This problem will be called rank-constrained-affine-feasibility problem. The motivation for the analysis of the rank minimization problem comes from the physical application of phase retrieval and a reformulation of the same as a rank minimization problem. We show that, locally, the method of alternating projections must converge at linear rate to a solution of the rank constrained affine feasibility problem.
Business Promotion through Instagram using Semi-Supervised Topic Modelling

The rise of social media, particularly Instagram, for the purpose of digital marketing has brought together both businesses and consumers. While businesses generate revenue and gain popularity through Instagram posts, the target audience benefits from these services and gives feedback about the product. The present study focuses on promoting businesses such as Event planning, Interior designing, Photography, and Makeup Artists through Instagram accounts using a Topic modelling approach. The clustering of user accounts was carried out using a combination of UMAP and KMEANS for automatic labelling which resulted in an accuracy of 87%. The generated labelled dataset is further sent to the LSTM model for the classification of user accounts for various services. This had an accuracy of 93%. An optimal semi-supervised model proposed in this study is built using UMAP, KMEANS and LSTM. This model outperformed many existing traditional approaches showing an accuracy of 81%. This study can find applications in several domains and help Instagram users promote their businesses through a digital platform.
Internal Model Control Scheme Based PV Battery Charging System Utilizing Buck Converter for EV Application

This paper presents the control of photovoltaic (PV) voltage using the Internal Model Control (IMC) technique, for battery charging systems utilizing the buck converter. The system consists of photovoltaic (PV) array, buck converter and battery. It has been modeled using small signal analysis. The proposed controller results fast and accurate tracking of reference PV voltage. This reference voltage is decided by MPPT under varying environmental conditions. To track this reference voltage, proposed IMC controller provides gate signal to buck converter. It exploits the full capacity of the PV panel. Also, it reduces switching stress, settling time and losses. Which cannot be achieved through direct duty cycle control (DDCC) of converter through Maximum Power Point Tracking (MPPT) algorithm. The efficiency of the proposed battery charging system has been evaluated under varying environmental conditions using MATLAB/Simulink. Finally, hardware results have verified the correctness and effectiveness of the proposed control strategy.
Comparing the predictive efficiency of Fama-French Three-Factor Model and Carhart Four-Factor Model: Financial market approach

This study aims to compare the efficiency of predicting return on portfolios of the Fama-French Three Factor Model (FF3) and the Carhart Four Factor Model (CF4) using the Machine Learning approach. Experimental study on Hanoi Stock Exchange in the monthly cycle of 1/2015 to 12/2022. The securities are grouped into 10 diversified portfolios by beta every 6 months. The study divided the data into 2 train-test sets at the ratio of 70:30. The results show that, although the Root Mean Square Error (RMSE) of the CF4 model is lower than that of FF3, the T-test results show no difference between the two models.
Adaptive Control Policy Via Switching Controller for Load Frequency Control Using Improved Particle Swarm Optimization

There have been several load frequency control techniques presented and reported up to the present time. Each technique has a unique manner of enhancing the capacity to reject disturbances and the system response. Some approaches are superior in some ways while others are superior in others. Using some of them together may bring better system performance. This research presents an adaptive control scheme for load frequency using a switched controller with improved particle swarm optimization. It will enhance the system's accuracy and performance. According to the control policy, Controller actions can be increased or decreased by giving them different weights during the operation. Two distinct Proportional-integral-derivative controllers from the current era are in use, and they were designed in two different ways. The best features of these two candidate controllers are extracted using adaptive control logic. To verify the accuracy and efficacy of the proposed policy, single-area power system problems are considered.
Embedded System Software Reliability Estimation During New Product Development

Software reliability estimation for systems operating in the field has been extensively discussed in the literature and several models have been developed. However, this approach of reliability estimation is a reactive approach, where the product has been deployed in the field and field returns are monitored for software issues. An area where there has been limited focus is on the evaluation of software reliability during the development phases of the software. The objective of this paper is to develop and present a methodology for embedded software reliability estimation before it has been deployed in the field. This approach involves assessing reliability risk at the requirements phase, and a method for mitigating and quantifying mitigation amount for reliability estimation through model-in-loop, processor-in-loop, and hardware-in-loop tests. This is a software reliability estimation through proactive risk mitigation strategies and enable guidelines for organizations on the software reliability at product launch.
Flight Delay Prediction Using Machine Learning

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81.