Miracle-dz/UniFAR-FTUs huggingface dataset:
A least-squares method of interpreting magnetic anomalies caused by two-dimensional structures

The equations relating the magnetic anomalies to the shape and susceptibility of a body are nonlinear with respect to the coordinates describing the shape. Therefore, iterative procedures must be used to obtain least‐squares estimates of the body coordinates. One method in general use for obtaining nonlinear least‐squares estimates is the Gauss method. This method often fails when the initial values for the structures and susceptibilities do not adequately account for the magnetic anomalies. Another method known as the steepest descent method generally converges to a solution; however, a large number of iterations are required. A method suggested by Marquardt (1963) incorporates the best features of the previous methods. In this paper the Marquardt method is applied to the interpretation of magnetic anomalies. For this purpose the two‐dimensional formulas derived by Talwani and Heirtzler (1964) are used to relate the geometry of a body to the resulting magnetic anomalies. The procedure efficiently control.
Localization of Fiber Cable with Distributed Acoustic Sensing

Submarine and underground optical cables have been indispensable information transmission pipelines for decades. It is critical to know the precise position of these cables for maintenance and repair, but current fault location methods are usually difficult and costly. To find the geographic location of these cables, distributed acoustic sensing with angle of arrival estimation is brought into fiber cable positioning for the first time, to the best of the authors' knowledge. In this paper, the geometric relationships between known sound sources and unknown cable positions are derived for two-dimensional fiber cable direction, and proof-of-concept experiments are carried out to validate the approach. Consequently, two-dimensional fiber direction tracing is realized with 0.02m error.
An Empirical Study on Fake News Prediction with Machine Learning Methods

Due to advancement in technology and distributed networking, there is huge information available on the internet. Due to this, it is possible that some users may try to post fake news through some platforms to get the financial credibility. A common user finds it difficult to differentiate the fake news in comparison with the authentic news. Due to this, a fake news can be main agenda against a particular individual, society, organization or even related to political party. To date, lot of research has been done to detect the fake news on the internet. But, most of the solutions are proposed by comparing with very few performance metrics along with limited data sets. In this work, we propose to use Decision tree, SVM, LSTM, Naive Bayes techniques to analyse and observe the behavior on different datasets. Furthermore, we compare and demonstrate the best approach through experimental analysis.
First Steps towards a Methodology for Unified Graph's Discrepancy Analysis

Researchers and IT professionals frequently use dataset comparison during software analysis. Additionally, they commonly make judgments based on discrepancies between two representations of the same item's set. To locate the error-prone areas of the system, developers may evaluate the densely linked regions of method call graphs in the context of their position in the package hierarchy tree. A universal technique for graphs, which can be utilized to unify the underlying process of discrepancy analysis, might help with these types of analyses. In this paper, we present a methodology for unified graph's discrepancy analysis, named Unigda. Its foundation is the previously established domain-specific discrepancy identification approach for cluster comparison. But to capture the similarity structures between the vertices of arbitrary graphs, we use several kinds of characteristic functions. 11Project no. TKP2021-NVA-09 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme.
Movie Recommendation using Collaborative Filtering to Make Accurate Predictions

A recommendation system that suggests appropriate movies to customers based on their prior interactions with the platform will increase customer happiness and, as a result, increase revenue for online streaming services like Netflix. The methods we'll cover here can be used for any product for which you wish to create a recommendation system; they are not just restricted to movies. In this paper, a collaborative filtering framework is used to provide recommendations to the users. We have used three different algorithms on the rating dataset. These are cosine similarity, K nearest neighbors (KNN), and Singular Value Decomposition (SVD) to predict the outcomes. The results are obtained in the form of Root Mean Square Error (RMSE), precision, and recall of all algorithms and at the end, we have done a comparison between these algorithms to check which algorithm is better. Many e-commerce websites like Amazon, Flipkart, and Mantra use this technique to know their customer behaviour and provide recommendations to them about the products that they are most likely to buy.
MRI brain tumor classification using robust Convolutional Neural Network CNN approach

During the last decade, the deep learning depending upon the Convolutional Neural Network (CNN) structure has been demonstrated as an efficient approach to classify different objects properly, and it has wide significant advancements in computer vision algorithms as well as increases in processing hardware. This research work aims to utilize the developed and evaluated Magnetic Resonance Imaging (MRI) technique for the predication of brain tumor employing simplified Convolutional Neural Network CNN structure. The developed approach uses less memory and processing power while handling huge datasets such as Real Brain Dataset. The recognition algorithm can rapidly distinguish an object within few seconds, and the maximum accuracy reaches to 98% for a good CNN structure setting option. The model being fit to the data till no additional remarkable reduction in the loss function value was performed upon the data of confirmation. It was trained and confirmed till (100) epochs upon the precision of the 70% of the (154) image samples and the testing of 30% image samples correspondingly with 10-cross fold validation. The measured classification accuracy of the brain tumor at the last epoch of the classifier training of validation data is virtuous.
NPSR: Neural Network enabled Phase-Space Reconstruction for Wireless Channel Prediction

In the massive multiple input output (MIMO) scenario of 5G wireless communication, the channel aging problem in high-speed mobile scenarios or rapidly changing channel propagation environment will significantly affect the performance of the communication system. Based on chaos theory, this paper proposes neural network enabled phase-space reconstruction(NPSR) to predict the channel state in MIMO, which alleviates the influence of channel aging. First, we show that the channel sequence is chaotic and obtain the reconstruction parameters of delay time and embedding dimension. Then, we combine the reconstructed phase-space and the back propagation (BP) network to predict the MIMO channel variation under different user moving speeds. We prove that the channel sequence is chaotic by using the Lyapunov exponent. In high-speed scenarios, NPSR outperforms the empirical chaos algorithm (ECA) and the autoregressive moving average (ARMA) algorithm. Compared with ARMA algorithms the average relative error of the prediction accuracy is reduced by 8.15%. Finally, we design an adjustable parameters configuration method based on ray features. The average time cost of NPSR in calculating phase-space reconstruction parameters is 37.6% of that of ECA.
Artificial Intelligence based Techniques in Respiratory Healthcare Services: A Review

Death rate associated with respiratory diseases is a global concern. The various challenges incorporated in respiratory health management are due to delayed and inaccurate diagnosis. The availability of voluminous healthcare data has generated the opportunity for the application of numerous artificial intelligence based methodologies. These methods have shown the potential to address and cope several concerns in healthcare like timely predictions, accurate diagnosis, precise monitoring, clinical support, robotic surgeries etc. In the recent years, the alarming rise in number of people suffering from respiratory health problems has become an issue of great concern but the appropriate diagnostic strategies may help to reduce the severity of the disease. This paper provides an overview of the prevalent usage of emerging artificial intelligence based techniques, with the primary focus on applications in respiratory healthcare domain, where it can be used to evaluate lung images as well as respiratory sound and textual datasets for diagnosis of disease.
Oil Well Monitoring System Based on IoT Technology and Machine Learning

The process of crude oil mining in oil wells takes a long time and requires good supervision to avoid unwanted things. This process requires full 24-hours monitoring of oil parameters such as oil temperature and flow rate. Currently, the supervision process is still done manually which may occur some errors. Based on this fact, this paper aims to design a surveillance or monitoring system that is more effective and efficient. The testing of this monitoring system uses an oil pump machine prototype with the assistance of a MAX-6675 temperature sensor and an ultrasonic flowmeter TUF-2000m as well as a sensor TM-1 transducer as an input tool. Raspberry Pi 3 as a microcontroller and a web application as an output that displays data in the form of graphs. The test stage is carried out by heating the temperature sensor, slowing down the flow of oil on the prototype, and checking the values displayed on the graph. The results of the test when the temperature sensor received heat, the microcontroller ran well, as evidenced by the data that was successfully stored on the web server and a graph showing the increase in the oil temperature value. Likewise with the flow rate sensor when it receives resistance or the flow is slowed down, the graph shows a decrease in the flow rate value in the oil. With the results of this test, the prototype of the monitoring system on oil wells with the Internet of Things (IoT) technology runs as expected, namely being able to monitor the value of oil parameters in real-time which allows effectiveness and efficiency in work to increase.
Descriptive Analytics Solution for Attack Detection by Utilizing DL Strategies

An intrusion detection system that employs a variety of system tasks and log files that are being generated on the host machine to detect HIDS refers to high-intensity distributed denial-of-service attacks. To enhance the capacity of intrusion detection systems, Big Data with Deep Learning Methods are combined. Deep Neural Network (DNN) and highly proficient approaches, Random Forest as well as Gradient Boosting Tree, are utilized to categories internet traffic datasets. Deep learning algorithms are widely used to develop an intrusion detection system (IDS) task of automatically recognizing and characterizing attacks at the host addressing performance in real time. Researchers utilize a homogeneity measure to analyze characteristics to identify its most productivity and organizational from dataset. As according to extensive experimental research, DNNs outperform classical machine learning classifiers in terms of performance. The findings shows that DNN has a good precision for different classifiers detection on datasets with accuracy rate for multi-class categorization. Employing Apache Flink to simplify the process and handling the streaming capabilities.
Player Engagement Classification in Mobile Games Using MUSE Headband

Mobile games have replaced computer games due to their popularity and accessibility. The increasing trend of mobile and online games has transformed gaming culture. The success of games requires them to have appealing gaming interfaces, optimal player interactions, and engaging playability characteristics. Therefore, analysis of a game player's engagement in any game is important to determine the effectiveness of any game. This study presents an electroencephalography (EEG) based player engagement classification model by utilizing MUSE Headband. EEG data were acquired from 31 players while playing three (Tetris, Subway Surfer, Smash Hit) games. A questionnaire was filled out to label the acquired data for supervised classification. Data was labeled into three classes (bored, engaged, and excited) based on a score of questionnaires. Two classifiers support vector machine and multi-layer perceptron were used to identify the engagement level of players and accuracy of 86.2% and 80.4% were achieved respectively. A comparative analysis of current methodology with the previous research techniques depict that currently proposed methodology is more effective in terms of level of classification accuracy, robustness and flexibility due to its applicability on variety of games. This shows the potential of our system to be used in in-game engagement analysis for a better player experience and thereby the success of the game in the market.
High-efficiency broadband and multiband cross-polarization conversion using chiral metamaterial

Metamaterials with tailored electromagnetic properties present incomparable advantages in manipulation and control of electromagnetic polarization states. Here, we report a unique chiral metamaterial (CMM) composed of three layers of metasurfaces. Taking advantage of the Fabry–Pérot-like interference effect, the suggested CMM is capable of achieving high-efficiency broadband or multiband cross-polarization conversion for different linearly polarized incident waves, while also showing strong asymmetric transmission effect for the opposite propagation directions. With the specially designed geometry, the present CMM can even simultaneously accomplish nearly 90°-polarization rotation for both the x- and y-polarized incident waves, thereby enabling more opportunities for designing high-performance polarimetric devices.
Security Risk Assessment & Mitigation Framework for Cloud-based IT Systems

More often than not, the need for security is realized only after the system has been broken. Adding security afterwards is often difficult and much costlier and sometimes, nearly impossible. Hence, Security needs to be embedded into an IT system in order to develop secure IT systems which is possible only when a Security Engineering Framework is well defined for the entire lifecycle of an IT system. In this Paper, we have designed and developed an implementable Framework for Security Engineering of Cloud-based IT Systems. Cloud-based IT Systems are vulnerable to a plethora of cyber-attacks because of their accessibility and their services exposed in a multi-tenant environment. The Cloud-based IT Systems Security Risk Assessment & Mitigation Framework (CISSRAMF) is agnostic to Cloud Service Providers (CSPs) as well as to the type of Services provided, i.e., Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). CISSRAMF encompasses the entire lifecycle of any Cloud-based IT System right from the conception phase till decommissioning of the system. CISSRAMF utilizes the Standards for Information Security and Cybersecurity like ISO/IEC 27001:2013, ISO/IEC 27002:2013, NIST SP 800-53 Rev. 5 and OWASP Application Security Verification Standard (ASVS) Project.
Random Forest Classifier Evaluation in DDoS Detection System for Cyber Defence Preparation

Cyberattack has become common problems in network security. One of the common techniques of the attack is Distributed Denial of Service (DDoS). A DDoS attack happens when the attacker is sending a huge amount of network requests to the connected host from many different sources. The attack can cause the network service disrupted and cannot be used due to overwhelmed machine that tried to serve the request from many sources. The impact of the attack can cause the network to be unavailable and can lead to user dissatisfaction. Therefore, the detection of DDoS is needed for maintaining the influx of network services and preventing the flooding of unwanted traffic to the host. The detection technique of a DDoS attack must distinguish between legitimate traffic and botnet traffic. The technique used for detecting the traffic that causes DDoS attacks will be using a machine learning algorithm. One of the implemented algorithms is the Random Forest technique. This study is focusing on evaluating the Random Forest implementation as the network classifier. The result of this study is determining the effectiveness and accuracy of the Random Forest Classifier. This algorithm also is compared with other algorithms. The evaluation has resulted in Random Forest Algorithm having better performance based on the variable that is related to accuracy and processing time than other compared algorithms, with a tight difference from the J48 algorithm. This study is contributing to the enhancement of machine learning implementation on DDoS attack detection and as part of cyber defense preparation for the stakeholders.
Integrated Deep Learning System for Car Damage Detection and Classification Using Deep Transfer Learning

The process for claiming car insurance in Indonesia often takes a long time. Therefore this research aims to speed up the process of car insurance claims by making an integrated deep-learning system. Three main tasks can segment car damage, detect damaged parts, and classify the severity of car damage. The dataset consists of 3 parts; the damaged segmentation dataset, the car parts segmentation dataset, and the car damage classification dataset. The models used in this paper are trained using deep transfer learning methods to save time and resources. The model for the segmentation task is Mask R-CNN, while for the classification task, we compare EfficientNet and MobileNetV2. In this paper, we try to increase the F1 score by adding a concatenate simple CNN and using the output from damage segmentation as an additional input for the classification model. Experiments show that Mask R-CNN is capable of detecting damaged car parts. Concatenate simple CNN modification on the classification model can boost the model up to a 9% average difference on the F1 score. The best classification model is MobileNetV2 which uses simple CNN modification with an F1 score of up to 91%.
Improved Bird Sound Classification Based on Deep Cascade Feature

In this paper, we propose to classify bird sounds based on the deep cascade feature. Specifically, we first construct the multi-view spectrogram. Here, the first representation is log-Mel spectrogram. In addition to log-Mel spectrogram, we apply harmonic-percussive source separation to obtain harmonic and percussive components as the second and third representations. Next, we use VGG16 as the feature extractor to obtain features. Furthermore, mix-up is used to improve classification performance. Experimental results on 43 bird species demonstrate that our proposed method can effectively increase bird classification performance. The best model can achieve a balanced accuracy of 90.56%.
Novel Approach for Group Communications using various Cybersecurity Techniques

Internet surfers and smartphone owners frequently utilize chatting software, although these software applications are built with security encryption, cyber-attacks are created to break this encryption. This paper is concerned with the finding of the best cryptography algorithm, which is better safe for assaults, simple to encrypt and has more security features. When a message is being transmitted, message security is a crucial factor to take into account. Many cryptographic methods are used to do this. We will be comparing different types of cryptographic algorithms and coming up with a better and most secure application. We opted to compare Ron Rivest, Adi Shamir and Leonard Adleman algorithm (RSA), the Triple Data Encryption Standard (3DES) algorithm and the Elliptic curve cryptography(ECC) since these three algorithms are the ones that messaging platforms utilize the most. As previously said, we'll use a few metrics to compare different cryptographic methods.
Political Campaign Strategy on Social Media Using Finite State Machine

Political campaigns are routine activities leading up to the General Election. Each election will be preceded by a campaign period to attract the sympathy of potential voters. Modern campaigns take advantage of technological advances that allow campaign activities through social media. Campaigns using social media have different challenges compared to conventional patterns. How to get social media users interested in joining campaign groups and actively interacting is a challenge for the candidates. This study was conducted based on data observations and experiences as a campaign team. This study proposes a simple way to build working guidelines for the campaign team. This guide was created using the Finite State Machine (FSM). A finite State Machine (FSM) is a model-building method that can describe sequential logical sequences and is widely used in games for artificial intelligence. This study uses FSM in the form of designing a simple game scenario. There are three scenarios developed, one main process and two sub-processes. These scenarios are the main activities in managing campaign groups on social media. It resulted in FSM, part of a severe game simulation guideline scenario for the campaign team.
Human Recognition Through Gait Audio

Human recognition through gait has always been a challenging field and to recognize a person through gait audio samples is even more challenging and exciting. In this present research, gait audio samples are utilized for person recognition in three different scenarios-normal, with a bag pack, and with shoes on. Gender estimation is also attempted and the results are quite satisfactory in comparison to existing techniques. In this work, gait audio samples are converted to image data specifically spectral representation, and fed to a convolution neural network for further processing. RESNET34 model is particularly utilized as a deep network. Gait Audio Dataset from TUM repository is used for this research. Recognition results reveal that the proposed model is quite better in terms of accuracy and provides motivation to explore CNN models for audio data.
Performance Enhancement of SoC with Five Port Router by Replacing APB Protocol

A In today's technological development and the advancement in IC technology, a huge number of intellectual property (IP)cores can be consolidated onto a single chip. Due to this, communication between the IP cores becomes more difficult. To overcome the restriction of this communication, we introduce a technology called NETWORK ON CHIP(NoC). This is an on-chip packet-switched network with IP cores connected to the network via interfaces, and the packets are sent to their respective destination to a multi-chip routing path. A router is an essential component for NoC architecture. The design had to be done effectively to build a competitive NoC architecture. In this proposed work router can be designed using Verilog. It has stored a forward type of flow control round robin arbitration and deterministic XY routing. The essential parts for a router are FIFO, arbiter, and crossbar. The plan behind the five-port router is intended to be used with the FPGA design platform to test the functionality of the NoC on hardware. The outline of the router is designed through Verilog and simulated using zynq board 7000 series and verified using system Verilog, and its feasible model is also verified.