Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison towards of Integration of Machine Learning Methods for Intrusion Detection Systems

It is important to incorporate modern approaches in that sequence to enhance the efficiency and quality of computer attacks identification. Recent years, machine learning methods are widely applied in Intrusion Detection Systems (IDS). We propose in this study compares two machine learning methods, namely Support Vector Machine (SVM) and Reinforcement Learning (RL). An analysis of existing techniques and their comparison regarding speed and precision, in addition to other factors may aid future researchers in understanding the recent advancements in IDS field as well as in creating innovations to satisfy needs and requirements in terms of computer security. The experimental results using the intrusion detection from NSL-KDD dataset show that the proposed integration is well suited for enhancing IDS performances.
Machine Learning Techniques for Heart Disease Prediction

To build a clear analysis of cardiac ailment, a complex mixture of scientific and pathological proof is regularly used. Because of this Doctors and pupils are keen to study with greater approximation a way to detect a coronary heart assault realistically and correctly. For this work, we created a cardiovascular disease prediction system that assists clinicians in predicting coronary heart contamination primarily based totally on affected person scientific statistics. Our plan is one of the 3 steps. Age, gender, form of chest pain, trestbps, cholesterol, fasting blood sugar, ECG rest, excessive coronary heart price, workout angina, age, inclination, variety of colored vessels, and all variables to consider. Second, we evolved more than one algorithm to distinguish coronary heart ailment primarily based totally on these scientific statistics. The precision of predictability is close to 80% of the time. Finally, we assemble a fundamental heart disease prediction system (HDPS). The HDPS could have some of the capabilities, inclusive of scientific statistics entry, an issue for showing ROC curves, and a predictive overall performance indicator (overall performance time, accuracy, sensitivity, clarity, and predictive outcome). Our procedures can forecast the chance of an affected person having coronary heart ailment with an excessive diploma of accuracy. The HDPS hired for this study is a unique approach to detecting cardiac problems.
Chemical Shift Artifact in Center-Out Radial Sampling: A Potential Pitfall in Clinical Diagnosis

Center-out radial sampling is a fundamentally different sampling strategy that has the property of employing a unique direction for each readout. Off-resonance artifacts (of which chemical shift is an example) are typically described as blurring for this type of sampling, however a more precise description is that the point-spread function becomes ring-shaped (1,2). Characterizing the effects that this produces in images is important in clinical diagnosis since certain types of pathology or anatomy may be mimicked by the chemical shift artifacts of fat.
Development of reservoir fine evaluation system based on computer digital modeling technology

Digital reservoir refers to a multi-dimensional virtual reservoir data volume built on a unified data platform using multidisciplinary and multi-disciplinary research results and information. It is a technology for digital, visual and intelligent research and management of reservoirs. The development of reservoir fine evaluation system based on computer digital modeling technology is a new advanced technology in the field of reservoir characterization. The main purpose of this study is to develop a computer program to evaluate the fine structure characteristics in the reservoir and its relationship with various parameters (such as porosity, permeability, density). This study uses multidimensional data from multiple real oilfields and laboratory tests to evaluate different aspects of these reservoirs, including their physical properties, such as porosity, permeability, density, and other factors that affect their internal flow behavior.
Label Smoothing Loss with Dual-Stream Network Using Separable Convolutional Layers for Retinopathy Grading and Classification

Retinopathy detection based on deep learning methods is a challenging problem, especially the diabetic retinopathy (DR) brings so many technical complications in medical image processing. Recently, label smoothing regularization has proved to be a better option to improve the performance of deep learning models. Therefore, in this paper, we introduce a dual-stream multi-task learning model along with a novel weighted label smoothing regularization loss (WLSRL) to detect retinopathy. The proposed model uses a dual-stream network by incorporating separable and conventional convolutional neural networks to detect diabetic retinopathy. The model is designed to classify numerous retinal diseases on two different types of data. The data $\Delta_{1}$ is based on stereoscopic fundus photographs and $\Delta_{2}$ consists of OCT-based retinal images. The model is trained and tested on both data separately. We perform two classification tasks TF1, TF2 on $\Delta_{1}$ and TO1, TO2 on $\Delta_{2}$. The task TF1 is for the classification of fundus photographs as normal and abnormal, whereas TF2 is for DR grading. Similarly, the task TO1 classifies OCT-based images into four classes, whereas the task TO2 classifies images as normal and abnormal. The empirical results show that the model achieves competitive results for retinopathy classification and grading using multitask learning with WLSRL.
Deep CNN based Road Sign Recognition for Vehicles using Raspberry Pi 3B

A key component of a system of intelligent transportation (SIT) is the method for recognizing road signs. Having the ability to recognise traffic signs precisely a nd effectively can boost driving safety. It primarily aims to detect and organize signs based on their circular shape. A pre-processed image is first used to highlight important details. Also the Hough Transform is used for region recognition and segmentation. On the basis of deep learning, a traffic sign identification method is proposed in this paper. The recognized street signs include then categorized using deep learning. This research proposes a detection of road signs and identification technique based on image processing that combines Convolutional Neural Network (CNN) with road sign classification. Due to its high recognition rate, CNN may be used to solve a number of computer vision jobs. Tensor Flow is used to implement CNN. The proposed model recognizes the circular sign with the greatest accuracy in the German data sets.
Deception Detection using a Multimodal Stacked Bi-LSTM Model

In the scientific community, researchers have recently become interested in the automatic identification of deceptive actions because of the range of fields where it might be advantageous, including criminology or security. Deception detection in conversational speech has drawn much attention in recent years. Given the significant risks associated with trial outcomes, using precise and efficient computational methods to assess the accuracy of court evidence may be very beneficial throughout the decision-making process. This study discusses about spotting deception in trial data from actual cases. Doing this has some challenges associated with creating a robust model that can accurately classify the deception and perform this action as fast as possible. Due to the limited number of videos and datasets available, some models overfit the data. A model should exist which can classify the data using various modalities, i.e., video, audio and text, and be able to work on multiple different datasets with excellent accuracy. This study has used videos from actual trials that were collected from open court proceedings and some videos from other datasets. To design a robust deception detection system that discriminates between witnesses and defendants, genuine and fraudulent testimony, this study investigates the utilization of text, audio and video modalities. By extracting and integrating information about the spoken words from audio, this study can achieve an accuracy of 80% approximately. The proposed model results with a classification accuracy of 96% approximately in an extended approach to perform video transcriptions. The Bag-of-lies dataset, a multimodal database captured in real-world settings has achieved an accuracy of 85%. The Miami University Deception Detection Dataset focuses on people telling truths and lies about their social relationships, achieved an accuracy of 98.1 % on the presented model. The proposed model employs LSTM (Long-Short Term Memory), Bidirectional LSTM (Long-Short Term Memory), CNN (Convolution Neural Network), and RestNet50. The results demonstrate that the proposed algorithm performs better at detecting deception than humans.
Game-based Learning approach and Serious Games to learn while you play

Serious gaming appears to be a promising research area with applications in education. In this article, trying to investigate new pathways in the evaluation of game design, a proposed framework variation is presented that is based on the frameworks proposed by Slimani et al., and Garris et al. Furthermore, specifically chosen cases of serious games are described in detail, to show how game-based learning and serious gaming can be combined efficiently in the educational context. The final part of this study contains an experiment on human motivation, with a sample of 50 student participants in the age group 18–25. The participants are questioned to three basic questions with basic objective to examine to what extent their three basic needs of competence, autonomy and relatedness are fulfilled. The findings show a positive attitude of students to the evaluated serious games and their utilization for educational purposes.
Multiple Antenna Systems in Mobile 6G: Directional Channels and Robust Signal Processing

In mobile communications, the trend since later fourth generation systems has been toward significantly larger numbers of antennas and higher carrier frequencies. This has led to numerous refinements and extensions of the channel models used, to account for newly encountered propagation characteristics. With increasing antenna numbers, the acquisition of channel state information (CSI) becomes more challenging, such that robust multiple antenna signal processing is required to cope with CSI imperfections. This article discusses important wireless channel characteristics and modeling aspects of fifth and sixth generation (6G) mobile, examines how these developments impact corresponding CSI acquisition techniques, addresses recent robust multiple antenna signal processing approaches, and specifically highlights features and potentials of 6G technologies.
Forecasting the Energy Utilization in WSN by Support Vector Regression Model

Energy management issues in Wireless Sensor Networks (WSNs) are an important research topic because they are widely used to monitor a variety of physical conditions. Though several analytical methods are available, it is still challenging to make a precise model to forecast energy utilization due to the prompting parameters' difficulty. It is critical for an energy manager to accurately predict energy consumption in order to make sound decisions and achieve energy utilization. The dimensionality reduction and efficient feature selection process in WSN is challenging. In many real-time situations, machine learning methods such as Support Vector Regression (SVR) provide greater accuracy. This paper proposes a Support Vector Regression (SVR) model for forecasting the processing energy utilization in WSN. The SVR system maps the input variables to high dimensional feature space and discovers the nonlinear connection between the input and output by reducing the data load. Furthermore, the Boruta algorithm is used to select the feature and improve the forecasting accuracy. The experimental outcomes demonstrate the FSVR method enhances the accuracy and minimizes the processing energy utilization in the WSN.
Skyline Computation Based on Previously Computed Results

Many methods are used to retrieve relevant information in big data. One of these is the Skyline operator, which is used to retrieve the best objects in multidimensional datasets. The Skyline result helps to extract the required data with the optimal combination of characteristics of the data efficiently. In real big data, the data is often updated, and new data can be added deleted, or updated. A required recomputation of the Skyline each time the data is updated may lead to unacceptable response time. In this paper, we focus on reducing the Skyline recomputation time every time the dataset is updated. We proposed an approach that benefits from the overlap of precomputed Skyline results. And for this purpose, we used the history of Skyline computation results to recompute the new Skyline after updating the data. Based on the experiments we have performed; our approach can significantly reduce the Skyline recomputation time every time the data is updated.
Energy-Efficient Cooperative Image Transmission in Multi-Hop Wireless Multimedia Sensor Networks

In scalar wireless multi-media sensor networks, almost all the nodes are connected via the internet (IOT devices). The amount of image, video and data transmission has increased significantly. The count of sensor nodes in a network has also increased, but still using an antiquated system for data compression and transmission may lead this well-driven network to work as same as something that started from scratch. To vanquish this, the best compression technique with a well-driven methodology is needed. This research work proposes an internet-based image-encoded method Coherent Energy Cooperative Image Transmission (CECIT) to achieve a better compression rate with lower energy consumption. Further, the performance of CECIT is improved by effectively utilizing the auto-correction code feature present in the QR code. Upon comparing the suggested technique to state-of-the-art algorithms, the simulation results demonstrate that it performs better and uses around 24.4% less energy.
A Review on Sentiment Analysis using Machine Learning

Sentiment Analysis [SA] is a method that is used to detect the state of some information or text. It can be applied to tasks such as text classification and summarization, and various methods can be used to extract sentiment from text, including machine learning algorithms and rule-based systems. Sentiment analysis is an important tool for businesses to understand customer feedback and measure customer satisfaction and loyalty. It classifies the given text into three categories: positive, negative, or neutral. The statement “the picture quality of TV is good” expresses a positive opinion about a particular TV, while “the sound is not adequate” expresses a negative opinion. Recent challenges with sentiment analysis indude capturing sarcasm, emotion in text and dealing with long texts. The paper presents a literature survey of many machine learning techniques as well as deep learning models such as CNN, LS TM, etc.
Machine Learning based Mobile App for Heart Disease Prediction

The world's leading cause of death is heart disease. A variety of modern technologies are utilized to treat cardiac disease The most common problem in medical centers around the world is that many medical personnel lack equal knowledge and courage to treat their patients, so they develop their own opinions, which leads in bad outcomes and, in some cases, death. Predictions of cardiac illness are employed to overcome these issues. This study has used various criteria to predict cardiac disease. These characteristics are Age, Gender, Cerebral Palsy (CP), Blood Pressure (BP), Fasting blood sugar test (FBS), and so on. The major goal of the research is to create a mobile app that reduces the cost of medical tests while also avoiding human bias. The outcome of the research is to forecast cardiac disease. The research made advantage of the built-in dataset and used PHR data to make predictions. Machine Learning is being used to build the model. This study utilizes a variety of machine learning algorithms, including Logistic Regression, ANN Multi-Layer Perceptron (MLP), and Random Forest (RF). Random Forest (RF) outperforms the other two algorithms in terms of accuracy. As a result, this study employs random forest to forecast heart healthand builds the mobile app with MIT App Inventor and stores the data in the Firebase database. The App could be to maintain personal health records and share our info with doctors. It will forecast heart health when you enter the criteria.
Spectral Frequency Analysis of Upper-Extremity Motor Imagery and Motor Movement Rhythms for Brain Machine Interface

A new and exciting field of study called brain-machine interface (BMI) is expected to aid with many issues, particularly for the disabled and paralyzed persons. A wheelchair or robotic arm can be guided in the appropriate way by detecting the left-hand (LH) and right-hand (RH) motor imagery (MI) and motor movements (MM). Electroencephalogram (EEG) sensors just on scalp can detect overall electrical activity caused by these alterations. The frequency spectrum of the EEG signals is crucial for the BMI implementation; hence the left and right hemispheres of the brain rhythms were used to extract the necessary information. For this study, two channels over sensorimotor areas with seven participants during the performance and/or kinesthetic perception of four actions have been chosen. The activity was shown using both MI and MM while being asked to open and close the left fist and the right fist. Using a Butterworth band pass filter (BBPF), the signals were filtered for the exact range of 8 to 30 HZ. The Fast Fourier Transform (FFT) is then used to investigate the knowledge in the frequency domain. Individuals differed in the MM and MI frequency range. The C3 and C4 electrodes for MM frequency ranges between 8 and 24 Hz are predicted by the algorithm and for the MI frequency ranges, the C3 and C4 electrodes are in the 8–26 Hz range.
Comparative Analysis for Blood Vessel Segmentation based on CNN Models

Retinal fundus images are images of color representing the inner surface of the human eye; they provide the anatomical structure of retinal blood vessels. Early diagnosis of eye-related diseases is crucial in order to take precautionary protocols to prevent major vision loss. In this study, a Machine learning-based approach for blood vessel segmentation is pro-posed. To this end, two different supervised Machine learning algorithms were implemented to analyze their performance and efficiency on blood vessel segmentation. These two algorithms are based on U-net modeling and ResNet50. A comparative analysis between the developed models and the state-of-the-art was conducted to determine a suitable solution for accurate blood vessel segmentation.
Personalized Rumor Refutation Through Graph Regular Pattern

Rumors spread rapidly across online social media and may cause severe economic loss and destabilize society. Considering different people are susceptible to different rumors, this paper proposed PRRM, a personalized rumor refutation mechanism based on individual interest and background knowledge. PRRM develop a regular rumor propagation pattern to characterize rumor spreading. Then we design a reverse pattern querying technique to mine the regular pattern and a regular pattern matching algorithm to identify the people who are more susceptible to given rumors. Using real-life Sina Weibo datasets, we experimentally verify the efficiency and effectiveness of our methods.
A Review on Digital Twin Technology in Healthcare

Digital twin technology is the union of three systems - the physical, the virtual and the interconnecting link layer. It acts as a replica and provides real-time representation of real-world physical objects in a virtual format. Digital twins are promising when applied in healthcare. This brief review gives a schematic overview of the current state of digital twin, specifically in the healthcare industry with gaps to overcome the challenges in building smart healthcare ecosystem. A concise synthesis of the following aspects of digital twin provides a cohesive whole in this paper: new capabilities and functions of the digital twin in healthcare, technologies that support digital twins, with its applications. This study motivated by the current COVID-19 pandemic, focuses on the application of the Digital Twin technology in healthcare.
A Review Based On Comparative Analysis of Techniques Used in Precision Agriculture

Agriculture is defined as a branch of science related to soil cultivation, crops growth and animals rearing for food supply and production of wool among other products. It plays pivotal role on the GDP (Gross Domestic Product) of any country. One way to increase the Agri-based GDP is to use Precision Agriculture (PA) whereby information technology is introduced into traditional agriculture. Agricultural expert systems have progressed from basic record keeping to large-scale farm management information systems (FMISs) that are utilized for crop prediction, crop disease detection, farm scheduling, and water monitoring. Precision Agriculture based framework allows us to use proper amount of water, fertilizers and seeds thereby, increasing the productivity of the agriculture fields by monitoring the environmental/soil parameters of agricultural land such as: pH, soil temperature, soil humidity, soil moisture and atmospheric pressure. In this paper an extensive literature review has been done on the architecture, hardware, communication protocol, and data acquisition infrastructure for crop monitoring systems along with the survey of different mobile applications and machine learning models used in precision agriculture.
Synthetic aperture radar for imaging through cloud layers

Recent military conflicts have demonstrated the need for close air support from precision attack platforms (e.g., the AC-130 gunship) to support ground forces. These aircraft typically use IR sensors to engage moving ground targets, such as vehicles and dismounts. Under clear conditions, targets can be easily identified and effectively engaged. In degraded environments, however, the atmosphere can inhibit traditional electro-optical sensors. Close-in air support therefore can currently only be conducted during clear weather conditions. In many important parts of the world (such as the Korean Peninsula, Central America, Colombia, and the Balkans) clouds are present between 25 and 50% of the time.1 The amount of time in which US close-air-support aircraft can engage targets is therefore severely limited. In addition—even in clear weather— once targets are engaged from the AC-130, copious amounts of dust are raised. This dust—from explosions and incoming rounds—prevents targeting of adversaries from the aircraft and makes it difficult to track friendly ground forces in the area. We are currently developing a gimbal-mounted radar sensor that will provide high-frame-rate radar images. This data will be used for targeting operations when atmospheric conditions inhibit the use of electro-optic sensors. Our radar system can be used to image the ground, even through clouds and dust, at a sufficiently high resolution and frame rate to support the engagement of maneuvering targets. An artist’s depiction of our system in operation is shown in Figure 1. For our system, the radar must be able to create a highresolution background, and it must permit detection and accurate location of moving targets. Radar frequency selection has therefore been a key tradeoff decision. For synthetic aperture radar (SAR) systems with a given resolution, the frame rate is proportional to the frequency. Moving to a higher frequency therefore provides a higher frame rate (and a lower latency). Moving to a higher frequency also requires a smaller antenna Figure 1. Artist’s impression of a close-air-support aircraft with the Video Synthetic Aperture Radar (ViSAR) imaging the ground through a layer of clouds.