Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison Graph Neural Networks for Remote Sensing Scene Classification

Remote sensing scene classification has been a research hotspot in recent years, and convolution neural networks have been widely used in this field. However, remote sensing scene images have large-scale variance with regard to a specific category, making it still difficult to individually train and obtain an excellent classifier by adopting features of single sample extracted by a CNN model to make prediction. To tackle above issue, a novel framework named Comparison Graph Neural Networks (CGNN) is proposed for remote sensing scene classification. The framework constructs comparison graph based on sample features extracted by CNN model. Then CGNN is employed on the graph for sample comparison and aggregates node features according to node connection weights learned by metric-learning neural networks from node similarities. Experiments are conducted on the benchmark dataset and the proposed framework obtains competitive performance compared with powerful baselines.
Discovering Non-Metadata Contaminant Features in Intrusion Detection Datasets

Most newly proposed detection methods in intrusion detection incorporate machine learning models to distinguish between benign and malicious traffic. The models are validated on a handful of academic datasets and ranked based on their classification performance. This article aims to demonstrate that unbeknownst to the new models' authors, there are features in these datasets which heavily bias the results and obscure a realistic, reliable estimate of the separability of the datasets. This paper proposes a methodology to estimate the contaminating influence of a dataset’s features based on the concept of blind generalization. The novel methodology is subsequently used to assess the features of six widely adopted intrusion detection datasets. In each dataset, several features show a pattern where regardless of training attack class, the models blindly generalize towards all available attack classes with nearly identical classification metrics. These features provide undeserved boosts in the baseline classification scores for each dataset. By themselves, some contaminant features even push these baselines upwards of 90% accuracy (balanced).
A Demonstration Of Optical, Doppler and Thermal Sensor Fusion In A Portable Personal Safety Device

Technology and gadgets are omnipresent in today’s world and bringing people closer. Despite that, personal security is an area that still needs a lot of work. We see cases of assaults and harassment against women in public places almost everyday. Smartphones and other gadgets do not seem to be the solution, as in many cases the individual is incapacitated, or devices are snatched making them vulnerable. It is worrisome to see this continue in the 21st century society and definitely needs attention. In this paper, we demonstrate a novel sensor fusion scheme between optical, Doppler and infrared sensors in a portable personal safety device. This device monitors the environment surrounding the wearer and sends alerts in case of a possible threat. The device also has preventive mechanisms so as to deter the intruder. The novelty of the contribution is the autonomous tracking, alert and preventive procedures that can act as a deterrent to avoid an incidence in the first place. The experimental results indicate strong potential. We also highlight some of the observations and challenges encountered during the exercise.
Building Segmentation of UAV-based Oblique Photography Point Cloud Using DoPP and DBSCAN

The segmentation of building point cloud is the basis of fast three-dimensional city models reconstruction. A building segmentation method of UAV based oblique photography dense matching point cloud is proposed using density of projection points(DoPP) and density based spatial clustering of applications with noise(DBSCAN). First, the building facades are extracted according to the density of projection points by using the rich facade features, based on the analysis of different spatial target features. Then, the density clustering method is introduced to further segment the extracted building facades, so as to realize the monomer segmentation of building facade from UAV tilt photography point clouds. Experimental results show that the proposed method can achieve good results, and provide a new building segmentation method from UAV based oblique photography dense matching point clouds.
Design of S-band Frequency Tunable Substrate Integrated Waveguide Band Pass Filter with EBG Slots

In this paper, the design of a frequency tunable band pass filter using Substrate Integrated Waveguide (SIW) technology is presented for S-band using PIN Diodes. Frequency tunability is introduced by varying the biasing of the PIN diodes, which are connected across perturbing vias. This in turn varies the resonant frequency of the filter. The filter design includes a U-shaped Electromagnetic Band Gap (EBG) slot to improve the stopband and passband characteristics. The performance is analyzed by observing the S-parameters of the designed filter. Two tuning frequencies were obtained for the designed filter at 3.45 GHz and 3.70 GHz with insertion loss of −0.75 dB and −0.81 dB, respectively.
Trend Analysis in Landsat Based Scheme for Burned Area over Northwestern Region of India

Crop residue burning exhibits a significant problem to air pollution witnessed by the Northwestern region of India during the Kharif and Rabi seasons. Kharif and Rabi refer to the crop pattern adopted by Asian countries following the monsoon season. After Harvesting crops, stubble formed as a byproduct is disposed of and burned on the ground. Consequently, raised a need to take action corresponds to fire incidents with changing frequency, acuteness, and different dimensions that need large image collection and processing of huge data. The paper investigates the potential of Satellite images in Spatial and Temporal Resolution in Burned areas in Haryana, India. The work presents the trend analysis over Burned Area assessment based on Landsat satellite data. A detailed analysis was carried out and found that some districts have more burning activity. Top burned areas in districts were selected and compared. Hence, a Scheme based on Landsat data can provide insight and help identify the affected area for investigation by stakeholders like Government, R&D Department, Agencies, etc.
A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms on Consumer Reviews

For any organization involving consumers, reviews and feedbacks are quite important. For this purpose, the bulk of data is generated from various social networking sites in terms of reviews and feedbacks. In order to understand consumer’s perception about an item, this research scrutinizes various supervised and unsupervised machine learning algorithms on two data sets. A comparative analysis is made for deliberating the efficiency of these algorithms on distinct datasets for text classification. This research is an attempt to find the best fit classifier for consumer’s perception using sentiment analysis. So, in order to accomplish this objective, firstly text preprocessing techniques are applied on datasets then feature extraction techniques are applied on the processed data. Thereafter, classification and clustering are applied using supervised and unsupervised machine learning algorithms respectively. Further, these algorithms are evaluated and the result reveals that supervised machine learning algorithms especially Support Vector Machine (SVM) outperforms unsupervised machine learning algorithms for garments dataset. And Naive Bayes (NB), Logistic Regression (LR) outperforms unsupervised machine learning algorithms for restaurant dataset.
Classification Accuracy in Sentiment Analysis using Hybrid and Ensemble Methods

Sentiment Analysis (SA) is a relatively young subject of research in the science of text mining. SA is seeking for opinions, feelings, and subjectivity in the content that it reads. The main purpose of sentiment analysis is to look at product and service reviews to see how many ratings they get. The ensemble model (EM) is proposed in this paper to improve machine learning (ML) results by combining many ML models into a single optimal predictive model. To improve the accuracy of the data mining model, two or more analytical models that are related but not identical are run, and the results are aggregated into a single score. Ensemble methods such as bagging, boosting, stacking, and voting are commonly utilized. Voting is best for classification problems, while regression is usually done by averaging. As a result, the ensemble model using the majority voting technique is used to improve sentiment analysis classification accuracy. For classification, the proposed ensemble classifier incorporates the results of prior work as well as AdaBoosting in SVM. These algorithms’ classification results are fed into the majority voting procedure as inputs. Each classifier’s forecast for test phrases is taken, and the final output prediction with more than half of the votes is deemed the winner. The suggested method outperforms both individual machine learning algorithms and previously proposed hybrid methods in terms of classification accuracy.
An Effective Application to Identify Brain Tumor using Deep Learning Model

Brain tumor is one of life threatening diseases for humans and the treatment is challenging. Recently the disease diagnosis industry is seeing enormous developments. Brain tumors can be identified from Magnetic Resonance Imaging (MRI) images. There are existing techniques available for brain tumor detection using image processing techniques. Some recent studies used machine learning approaches for brain tumor detection. However, an effective model and application is required for this life threatening disease. Availability of dataset is an added advantage for these studies. Nowadays, large amounts of data can be preserved for research and these can be used effectively by deep learning models. Disease diagnosis through deep learning techniques are emerging these days. In this paper, brain tumor detection is proposed through a deep learning model, Convolutional Neural Network (CNN). Deep learning models are achieving good results on brain tumor detection. In this work, an application is proposed, in which users can upload the MRI image and detect whether it is a tumor or normal MRI. CNN based classification for brain tumor detection has achieved highest classification accuracy around 99.5%. Experimental results showed that high precision value 99.3% for optimized training epochs.
Diagnosing Covid-19 and Pneumonia from Chest CT-Scan and X-Ray Images Using Deep Learning Technique

The outbreak of Covid-19 (COV-19) has become one of the global severe public health issues. It is an ongoing global pandemic that has spread rapidly. It not only affected human beings but also paralyzed most industries. It is very critical to diagnose COV-19 & Pneumonia (PNA) because both are having the same sign & symptoms that correlate to the most extent. The (RT-PCR) Reverse Transcription- Polymerise Chain Reaction widely used official screening method for detection of COV-19, while radio-logical imaging CT Scan (Computed Tomography) of human chests is used for detection of PNA as well as for COV-19 diagnosis also. The method for detecting COV-19 & PNA using CXR (Chest X-ray) & CT scans is too time consuming for an expert while the result accuracy is less. In this proposed project the work is based on the transfer learning model utilizing the Deep learning module for COV-19 & PNA solicitation from radiological images of the patient suffering from PNA & COV-19 which plays critical role in early detection & classification of COV-19 & PNA. In the present work multi-model deep learning modules are used for the image detection are as follows: DenseNet 121, MobileNet, Inception V3, ResNet 50, & VGG 16. In this study, the data set contains X-Ray & CT-Scan Images which had been collected from CORD-19 & PNA Data sets. To evaluate the effect of dataset size based on the performance of multi-modal deep learning modules, to train the proposed module of deep learning using both the original & augmented dataset, the result were quite promising for DenseNet-121 for CT Scan with 97% accuracy while VGG 16 for CXR images with 99% accuracy.
Electromechanical SETS with RL technique

The carbon-emitting fuel reduction leads us to solar power and its possible way of maximum availability. Sun energy is the most often used copious energy source in nature. This can be received using receptors and transformed into electrical power by photovoltaic cells. The receiving method of energy is an important phase and it should be one of the best efficient methods. This paper follows the Sun positioning algorithm to get the exact location of the Sun for years and the tracker movement components are controlled by PID controllers that use a sophisticated, opposition-based chimp optimization algorithm with reinforced learning.
Reliability-Aware Computation Offloading for Delay-Sensitive Applications in MEC-Enabled Aerial Computing

With the advent of light and economical Unmanned Vehicles (UAVs), the Internet of Things (IoT) in air is gaining more popularity. However, IoT in air has its own set of requirements and challenges. It is unwise to upload legacy IoT applications to aerial computing networks. For instance, aerial computing in resource constraint UAVs demands energy-efficient solutions. Fortunately, mobile edge computing (MEC) can assist aerial computing by offloading partial or all applications to edge servers (ESs) in MEC. Nevertheless, ESs are subjected to finite resources, and there is a certain probability of failure, and the application in aerial computing is for high-reliability demands. Given the above issue, we investigate computation offloading for delay-sensitive applications under reliability constraints in MEC-enabled aerial computing network where the delay-sensitive applications are represented as scientific workflow applications. Technically, a two-phase reliability-aware computation offloading approach is proposed. More specifically, in the first stage, a multi-objective reliability-aware energy-efficient computation offloading strategy is proposed for the scientific workflow application. Secondly, an improved lazy shadowing scheme is proposed to enhance reliability, energy efficiency, and load balancing. Extensive experiments have been conducted to show the effectiveness and superiority of our proposed solution under different situations.
ANFIS-Controlled Voltage Source for Vigorous Speed Control of BLDC Motor Drive for Utilization in Smart Electric Vehicles

Technology is advancing at a very fast pace newer and better types of machines are being evolved. Initially, we had the DC motor but now we have a new generation of DC motor called brushless DC motor (BLDC). This has all the advantages of a dc motor with none of the disadvantages like commutation loss or sparking due to the commutation process. In this paper, we have devised a way for speed control of BLDC motor. The research is not limited to only tracking a single reference point but a fast-changing set of references. This is done to simulate real-life conditions where an electric vehicle that uses BLDC motor for mobility as on roads, we are certain to face sudden speed changes so the speed control system should be robust to be able to undergo such changes. Here we have used two types of controllers for controlling the voltage input to the motor. This includes a PID controller and a new improved which has a combination of both neural and fuzzy i.e., ANFIS controller. Transient response characteristics of both the controlling system are compared. A quasi-iterative process is used for tuning PID parameters. In conclusion, we can see that the ANFIS controller is better because it is easier to implement and has robust convergence characteristics. For simulation purposes, we have used a permanent magnet synchronous motor with a change in the back-emf waveform from the sinusoidal to the trapezoidal waveform.
Osteo-Net: A Robust Deep Learning-Based Diagnosis of Osteoporosis Using X-ray images

Osteoporosis results in the deterioration of bone tissues and this problem is prevalent among people all over the world, especially older people. The diagnosis of osteoporosis is frequently made clinically manifested by fractures linked with bone fragility. Thus, early diagnosis would be required to take proper treatment and eliminate excess fractures, lowering mortality and morbidity. The development of deep learning-based technique for diagnosing osteoporosis disease from bone X-ray images, a commonly available and low-cost image-based medical examination approach, is the objective of this study. A deep learning-based architecture, Osteo-Net with many blocks and skip connections is presented in this work. The proposed technique utilizes the robustness of deep learning models to extract high-level features from low-quality X-ray images. The trained model achieved a validation accuracy of 84.06% and testing accuracy of 82.61% on unseen test images with less training time. The proposed method is low-cost and computationally efficient. The experimental results show an excellent classification performance when used for osteoporosis screening and the high efficacy of the proposed method over other state-of-the-art methods. The proposed low-cost deep neural network-based approach could be utilized as a supplement to Dual-energy X-ray Absorptiometry (DXA) screening, particularly in primary health care centers with insufficient DXA machines.
Water Quality Monitoring Using IoT & Machine Learning

Safe water access is fundamental form of human survival and it is presented as a fundamental human right. As consumers use water, primarily sourced from pipes and springs located around towns, contamination, leakages, and pilferage happen. IoT and Machine Learning offer a promising solution to address these challenges. Premised on these technologies, the authors propose a system that monitors water quality and pilferage and wastage that uses machine learning algorithms for decision making.
Transmission Line-based Wave Digital Models of Inphase Gysel Power Dividers

The paper proposes a framework for the design of the wave digital (WD) model of three-port microwave device. To accomplish this task, transmission line (TL)-based wave digital approach is applied, i.e. basic wave digital building blocks like unit elements, circuit elements and interconnection networks are used. As a case study, traditional Gysel power divider and modified Gysel power divider are considered. Clearly, the formed TL-based WD models of these power dividers are in a very good agreement with such models formed in Advanced Design System schematic and HFSS electromagnetic simulation software.
A Plug-and-Play Deep Denoiser Prior Model for Accelerated MRI Reconstruction

Magnetic resonance imaging (MRI) reconstruction is one of the important inverse imaging problems. Unlike the classical MRI approaches which demand long scanning time and are prone to reconstruction artifacts, compressed sensing MRI (CS-MRI) generates the scans data relatively faster and produces less artifacts for medical diagnosis. Model-based CS-MRI algorithms require long reconstruction time to obtain an MR image. On the other hand, although training time of deep learning techniques for the task is rather long, their reconstruction time is much shorter compared to iterative model-based MRI algorithms. Moreover, recent works have shown that Gaussian denoisers including deep denoisers can be utilized to solve the inverse problems in a plug-and-play fashion. In this paper, we propose an iterative convolutional neural network based Gaussian denoiser as a solver for the CS-MRI problem. Our experiments show that the proposed method has better reconstruction ability when compared to some important model-based and deep learning based methods from the literature.
ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM

Aiming at the problems of low feature extraction efficiency and low detection accuracy of traditional ECG signal detection algorithms, this paper proposes a convolutional neural network (CNN) and bi-directional long short-term memory (Bi-directional long short-term memory, LSTM) network hybrid ECG signal anomaly detection algorithm. This model effectively utilizes the ability of CNN to automatically extract features and BiLSTM's ability to efficiently process time series data. Through experimental verification on the arrhythmia data set in the MIT -BIH database, the overall accuracy of the model is 98.56%. Compared with support vector machine (SVM) and bidirectional long short-term memory neural network (BiLSTM), the accuracy and F1 value of this model are improved.
Optimization of the Histogram Intervals Number which Approximate Brightness Probability Distributions in Stochastic Image Alignment Based on Mutual Information

This article investigates the influence of histogram intervals number on the accuracy and speed of the stochastic algorithm needed to solve the image alignment problem. The mutual information of images is considered as a similarity measure. Histograms are used for approximation of brightness probability density distributions when finding the gradient of image mutual information. Experimental results are obtained for the Shannon, Renyi and Tsallis mutual information cases.
Trajectory planning for automated high-way driving of articulated heavy vehicles

As the global level of CO2 in the atmosphere continues to rise, ways to decrease and stop this rising trend are needed. One potential measure to combat rising green house gases could be to use long combination vehicles, LCVs, for heavy duty freight transports. However, because of the length, weight and motion characteristics of LCVs, they become more difficult to maneuver, especially in dense traffic. The development of advanced driver assistor autonomous functions for controlling LCVs, could help ease LCVs inherent problem of maneuvering in dense traffic. In this project, the LCV considered is the A-double combination. An A-double combination consists of a tractor connected to a semi-trailer. Attached to this semi-trailer is a converter dolly onto which a second semi-trailer is attached. The objective for this project is to develop a trajectory planner for an A-double combination to allow for automated driving in a highway environment. Trajectory planning for automated vehicles consist of generating a trajectory that spans both the longitudinal and the lateral dimension. This trajectory is calculated based on the vehicle state and control evolution in time, given some set of constraints. There are two core maneuvers that needs to be handled by the trajectory planner, lane keeping and lane changing. The method used for developing the trajectory planner is based on numerical optimization due to its innate capability to handle constraints and its real-time compatibility. This way, safe and smooth trajectories can be generated. The control framework used is called model predictive control, MPC, and the trajectory planning problem is formulated as a quadratic problem with linear constraints. The trajectory planner was developed with the ACADO toolbox and then combined with the solver qpOASES. The generation of results was done in Matlab/Simulink by creating s-functions for the trajectory planner, and then simulating it against a high-fidelity plant model of an A-double combination. Two trajectory planners, one with a prediction horizon of 2 seconds and one with a prediction horizon of 5 seconds, were simulated and discussed. Both the planners successfully completed a pre-defined highway test scenario regarding safety, smoothness, actuator limitations and computational time. However, smoother longitudinal characteristics could be concluded for the planner with a prediction horizon of 5 seconds compared to the trajectory planner with a prediction horizon of 2 seconds. Overall, the proposed trajectory planner is able to handle the two main maneuvers of