bluuebunny/crossref_metadata_2025 huggingface dataset:
A benchmark of health insurance fraud detection using machine learning techniques
Authors: Cherkaoui Ossama , Anoun Houda , Maizate Abderrahim
Published: April 2024

Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%).
 
Control system optimisation of biodiesel-based gas turbine for ship propulsion
Authors: Machmudah Affiani , Bakar Elmi Abu , R Ranjendran , Nugroho Wibowo Harso , Solihin Mahmud Iwan , Ghofur Abdul
Published: April 2024

Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.
Activity recognition based on spatio-temporal features with transfer learning
Authors: K Seemanthini , N Shobha , Hiremath Shantala , Lakshmi B S Sowmya , Hiremath Jayaprada , Hiremath Mrutyunjaya
Published: April 2024

Human action recognition (HAR) has gathered significant attention recently due to its high demand in various application domains. We explore different CNN architectures for extracting Spatio-Temporal features. In addition to spatial correlation in 2D images, video actions also own the temporal domain attributes. Many research attempts addressed simple activity recognition, but complex activity recognition is still challenging. In the proposed work, the concept of transfer learning consists of two pre-trained networks, ResNet50 and VGG16, which are used to extract Spatiotemporal features and are later fused using the averaging method. Pre-processed frames are considered for the pre-trained network, the pre-processing consists of frame extraction, resizing, and contrast enhancement. The fused features are trained using a multiclass non-linear SVM classifier that achieved an accuracy of 89.71% on the UCF-101 database.
Fault tolerant control of permanent magnet synchronous motor based on hybrid control strategies DTC-SVM with second order sliding mode control using multi-variable filter
Authors: Bahiddine Miloud , Belhamra Ali
Published: April 2024

This paper describes direct torque control (DTC) of a permanent magnet synchronous machine (PMSM) powered by a two-level voltage inverter whose switching of switches is based on the Space Vector Modulation. To overcome the robustness of the control to the presence of a fault, we included an improvement of the direct torque control with Space vector modulation (DTC-SVM) by the use of the filters for the flux and the torque and compared to the direct torque control of the DTC-SVM, the PI controllers are replaced with sliding mode blocks, This control method allows giving a new structure DTC -SVM with sliding mode control. The analysis of the results shows good performances for the speed and a considerable reduction of the fluctuations at the level of the torque and the flux.
Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction
Authors: Moumen Idriss , Abouchabaka Jaafar , Rafalia Najat
Published: April 2024

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.
Enhancing the smart parking assignment system through constraints optimization
Authors: Elkhalidi Nihal , Benabbou Faouzia , Sael Nawal
Published: April 2024

Traffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hindering urban mobility particularly in the big city and other congested areas; Drivers lose a significant amount of time looking for looking for a parking spot. This leads to an increase in accidents, a big consumption of fuel and a spectacular augmentation of pollution. We present a parking assignment system based on constraint programming in this paper, to meet the need for effective parking management in smart cities, for a group of drivers booking in the same time and area. In this work, we suggest two formulations of the Parking Assignment Problem, The first was established by using Constraint Satisfaction Problems (CSP) and the second is based on Mixed Integer Linear Programing (MILP). An implementation of the model taking advantage of Choco solver dedicate to the constraint programming and the evaluation of its scalability compared to the Mixed Integer Linear Programing solvers. The experiments conducted with Choco and MILP solvers on a real case study in the city of Casablanca showed that the two methods generates promising solutions in terms of scalability and response time.
Feature selection techniques for microarray dataset: a review
Authors: Nagaraja Avinash , Sinha Sitesh Kumar , Mallaiah Shivamurthaiah
Published: April 2024

For many researchers working on feature selection techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create feature selection approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary feature selection techniques, such as Filter, Wrapper, and Embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on Critical Review Questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of feature selection strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment
From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation
Authors: Bensalah Nouhaila , Ayad Habib , Adib Abdellah , Ibn El Farouk Abdelhamid
Published: April 2024

Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.
Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.
Bangla song genre recognition using artificial neural network
Authors: Akter Mariam , Sultana Nishat , Noori Sheak Rashed Haider , Hasan Md Zahid
Published: April 2024

Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, Soundcloud or any other internet platform. Music Information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, Rock, Hip hop, Nazrul, Rabindra and Folk music. We have started with some traditional machine learning algorithms having K-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machine and Decision Tree but ended up with a deep learning algorithm named Artificial Neural Network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and Mean Chroma Frequency Values of that raw amplitude data. But we found that music Tempo having Beats per Minute value with two previous features present better accuracy.
Face mask classification using convolutional neural networks with facial image regions and super resolution
Authors: Wattanakitrungroj Niwan , Wettayaprasit Wiphada , Rujirapong Peemakarn , Tongman Sasiporn
Published: April 2024

Face mask classification is relevant to public health and safety, so an approach for face mask classification using Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, BSRGAN, for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.
Fabrication and characterization of methylammonium lead iodide-based perovskite solar cells under ambient conditions
Authors: Reddy Dwayne Jensen , Lazarus Ian Joseph
Published: April 2024

This study investigated the fabrication and characterization of CH3NH3PbI3 based perovskite solar cells (PSCs) using the one-step spin coating technique under ambient conditions, eliminating the need for expensive glovebox and thermal evaporation equipment. The perovskite layer was annealed at 65 °C for 30 seconds and 100 °C for 30 seconds, 1 and 2 minutes. The scanning electron microscope (SEM) images show a smooth and uniform surface coverage for the ETL and CH3NH3PbI3 layers. SEM results also show an average grain size of 397 nm for CH3NH3PbI3 and an average particle size of ~17 nm for TiO2 was confirmed by transmission electron microscopy (TEM). X-ray diffraction (XRD) results confirmed the formation of tetragonal perovskite (CH3NH3PbI3) phase with high crystallinity with a crystallite size of 19.99 nm for the samples annealed for 30 seconds at 65 °C and 1 min at 100 °C. FTIR results also confirmed the presence of anatase TiO2 at wavenumber 438 cm-1 and the formation of the adduct of Pb2 with dimethyl sulfoxide (DMSO) and MAI is confirmed at 1,015 cm-1 . From the Tauc plot the bandgap energy of TiO2 and Perovskite layers was determined to be 3.52 eV and 2.06 eV respectively. An open-circuit voltage was 0.9057 V and short circuit current density was 12.2185 mA/cm2 with a fill factor of 48.05 and power conversion efficiency (PCE) of 5.199%.
Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology
Authors: Mopari Shrikant S. , More Dagadu Shankar , Bhalchandra Anjali S. , Murthy Pannala Krishna , Jadhav K. M.
Published: April 2024

Nowadays, with the significant expansion of industrial growth, the bulk power requirement can only be satisfied through high-voltage direct current HVDC transmission. The converter transformer is the utmost vital part of the HVDC transmission. Pressboard insulation is most commonly used as inter-disc insulation in converter transformers. During working conditions due to elevated temperature and different operational stresses, insulation material gets deteriorated. It may cause a risk to the life of the converter transformer. The effects of elevated temperatures as well as frequency on pressboard insulation of the converter transformer are examined in this study. The condition evaluation and morphological changes in pressboard insulation at elevated temperatures can evaluate with the help of frequency domain spectroscopy (FDS) and atomic force microscopy (AFM) techniques. The impact of elevated temperatures on insulation material can be analyzed based on surface roughness and dielectric parameters. In MATLAB Simulink environment, a dual winding single-phase converter transformers valve side star winding 60 discs model is constructed for impulse test. Based upon arrival time and velocity of traveling wave, insulation degradation location can be identified by using mathematical morphology. The simulation results demonstrate that the suggested method can notably located degradation across disc winding.
A novel identifiable data sharing mechanism for multiple participants in cloud computing
Authors: Karemallaiah Jayalakshmi , Revaiah Prabha
Published: April 2024

Recent applications and growth on the internet have generated a lot of popularity and adoption of cloud computing which aims to assure the various computing resources. Data storage is one of the primary resources offered by the cloud; however, considering the multiple users in the particular cloud raises major concerns due to security. Recent researches shown great potential for providing efficient data sharing with multiple users. However, tracing of the data provider is still concerned to be a major issue. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism which aims to provide security for multiple users in a particular cloud group. At first, IDSMU creates the general participants (GP)-key for secure access to data. Further, IDSMU creates the trusted participants (TP) based on the reputation which further helps in creating the key generation. A novel signature scheme is used for identifying the participants; IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count.
Integrated electronic system for FET biosensor assessment based on current-voltage curve tracing
Authors: Bryantono Achmad Arif , Kamajaya Leonardo , Fitri Fitri , Sungkono Sungkono , Herwandi Herwandi , Fahanani Agwin Fahmi
Published: April 2024

Field-effect transistor (FET) biosensors are pivotal in diverse applications, from environmental monitoring to healthcare diagnostics. Current-voltage (I-V) curve tracing is a powerful method for evaluating FET biosensor behavior, enabling comprehensive analysis of their FET biosensor characteristics. Traditional I-V curve tracing methods often require complex and expensive equipment, limiting their accessibility and practicality for routine sensor assessment. This study aims to develop and demonstrate an integrated electronic system for assessing FET biosensors using I-V curve tracing. The integrated electronic system uses readily available components, including microcontrollers, analog circuitry, and user-friendly software. We developed a compact, low-cost device that generates I-V curves for the FET biosensor. The integrated electronic system successfully generated I-V curves for various FET biosensors. The system demonstrated consistent, reliable performance, portability, and ease of use, making it a practical solution for routine sensor assessment. The average error in measurements using bipolar junction transistors (BJT) and metal-oxide-semiconductor field-effect transistors (MOSFETs) results in 2.62%, and measurements at different pH levels have a sensitivity of 21.6 mV/pH and a linearity of 0.9892. This innovation contributes to the advancement of FET biosensor technology. In the future, the developments should focus on ensuring their accuracy and reliability in various sensor fields.
An efficient high throughput BCH module for multi-bits error correction mechanism on hardware platform
Authors: Puttaraju Rohith , Muniyappa Ramesha
Published: April 2024

The bose-chaudhuri-hocquenghem (BCH) codes are a cyclic error correction codes (ECC) class. The BCH is constructed by using a polynomial over the Galois field. The BCH codes can detect and correct the multi-bits with an easy decoding mechanism. The BCH codes are used in most of the storage device's cryptography, disk drives, and satellite applications. This manuscript presents an efficient high-throughput BCH module with an encoding and decoding mechanism for multi-bit corrections. The BCH code of (15, k) is used to construct the encoder and decoder architectures. The BCH encoder decoder (ED) module with single error correction (SEC), double error correction (DEC), and triple-error correction (TEC) are discussed in detail. The BCH encoder module uses a linear feedback shift register (LFSR). The BCH decoder with SEC and DEC is constructed using the syndrome generator module (SGM) and chien search module (CSM). The BCH decoder with TEC is designed using SGM, inversion-based berlekamp-massey-algorithm (BMA), and CSMs. The BCH-ED module with SEC, DEC, and TEC utilizes <1 % chip area on Artix-7 FPGA. The BCH-ED with SEC, DEC, and TEC achieves a throughput of 7.13 Gbps, 1.2 Gbps, and 0.803 Gbps, respectively. Lastly, the BCH module is compared with existing BCH approaches with better improvement in chip area, frequency, and throughput parameters.
Optimizing gula apong production with an IoT-based temperature monitoring system
Authors: Sahrani Shafrida , Awang Mat Dayang Azra , Abang Zaidel Dyg Norkhairunnisa , Hong Ping Kismet Anak
Published: April 2024

Determining the quality of gula apong is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The gula apong industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of gula apong. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality gula apong is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of gula apong to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of gula apong but also reduce the cooking time and cost.
Development and implementation of a Python functions for automated chemical reaction balancing
Authors: Dumka Pankaj , Chauhan Rishika , Mishra Dhananjay R. , Shaik Feroz , Govindaraj Pavithra , Kumar Abhinav , Sonawane Chandrakant , Velkin Vladimir Ivanovich
Published: April 2024

Chemical reaction balancing is a fundamental aspect of chemistry, ensuring the conservation of mass and atoms in reactions. This article introduces a specialized Python functions designed for automating the balancing of chemical reactions. Leveraging the versatility and simplicity of Python, the module employs advanced algorithms to provide an efficient and user-friendly solution for scientists, educators, and industry professionals. This article delves into the design, implementation, features, applications, and future developments of the Python functions for automated chemical reaction balancing. The functions thus developed were tested on some typical chemical reactions and the results are the same as that in the literature.
Pole placement tuning of proportional integral derivative feedback controller for knee extension model
Authors: Arof Saharul , Noorsal Emilia , Yahaya Saiful Zaimy , Hussain Zakaria , Radzali Rosfariza , Razak Faridah Abdul , Mustapha Harith Firdaus
Published: April 2024

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.
Plant pathology identification using local-global feature level based on transformer
Authors: Ha Manh-Hung , Nguyen Duc-Chinh , Do Manh-Tuan , Kim Dinh-Thai , Le Xuan-Hai , Pham Ngoc-Thanh
Published: April 2024

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.
Encrypted image processing using compression and reversible data hiding
Authors: Zine Yasmina , Boumehed Meriem , Said Naima Hadj
Published: April 2024

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.
TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning
Authors: Do Van-Dinh , Le Van-Hung , Do Huu-Son , Phan Van-Nam , Te Trung-Hieu
Published: April 2024

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.
Microstrip antenna system for communication capabilities applications
Authors: Galleto Jr. Fredelino A. , M. Africa Aaron Don , F. Tablada Alyssa Joie , Amadora Jr. John Ernesto G. , L. Burgos Ira Third , K. Borebor Alliyah Mae , S. Belandres Rocelle Andrea , L. Montaño Rafael Dominic
Published: April 2024

In this comparative study, seven different microstrip antenna shapes, including rectangular, elliptical, triangular, inset fed, H-notch, and E-notch, were observed and analyzed, focusing on their suitability for global positioning system (GPS) application in microsatellites. To enable meaningful comparison, the study utilized the optimal resonant frequency in GPS applications, which is 1.57542 GHz. All the antenna designs have been generated using MATLAB’s Antenna Toolbox and are 100% efficient under ideal conditions with zero polarization loss, which is assumed in the link budget analysis. The results show that each antenna shape has been found to offer distinct advantages and limitations. Along with this, the circular and elliptical patch antenna presented a well-balanced performance, which is suitable for GPS applications. However, the elliptical shape falls behind the circular shape, which was determined to be the most optimal choice for GPS application, providing excellent isotropic antenna gain, return loss, voltage standing wave ratio (VSWR), and strong link budget analysis results.
Blockchain technology integration in service migration to 6G communication networks: a comprehensive review
Authors: Al-Ansi Ahmed , Al-Ansi Abdullah M. , Muthanna Ammar , Koucheryavy Andrey
Published: April 2024

The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.
Privacy-preserving authentication approach for vehicular networks
Authors: Mulambia Chindika , Varshney Sudeep , Suman Amrit
Published: April 2024

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.
A device to device driven approach towards optimizing energy efficiency for 6G networks
Authors: Aneesh Sonia , Shaikh Alam N.
Published: April 2024

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-to-device (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication.
Optimizing high availability multi-controller placement in SDN/NFV 5G networks: a survey
Authors: Rasool Samer Mohammed , Boujelben Yassine , Zarai Faouzi
Published: April 2024

In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability.
Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach
Authors: AlHija Mwaffaq Abu , Alqudah Hamza Jehad , Dar-Othman Hiba
Published: April 2024

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).
Intelligent-of-things multiagent system for smart home energy monitoring
Authors: Vemuri Ratna kumari , Vijaya Durga Chinni Bala , Syed Ibrahim Syed Abuthahir , Arumalla Nagaraju , Subramanian Senthilvadivu , Bhukya Lakshmi
Published: April 2024

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.
Distributed denial of service attacks classification system using features selection and ensemble techniques
Authors: Bagdadi Leila , Messabih Belhadri
Published: April 2024

Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.
Random access memory page caching: a strategy for enhancing shared virtual memory multicomputer systems performance
Authors: Vyazigin Stepan , Mansurova Madina , Malyshkin Victor , Shaykhulova Aygul
Published: April 2024

This study examines a modified approach to optimizing the performance of support vector machine (SVM)-type multicomputer systems through a distinct type of caching method that allocates space in the random access memory (RAM) of a computing node for caching pages. The article extensively describes research on enhancing the performance of the SVM system through memory page caching in RAM at the hardware level by implementing the SVM system based on field-programmable gate arrays (FPGA). A systematic comparative evaluation highlights a discernible enhancement in system performance relative to systems not equipped with the revised caching algorithm. These findings could prove instrumental for subsequent studies focused on optimizing the performance of SVM systems, providing empirical data to inform future investigations and potential applications in multicomputer system performance enhancement.
Automatic translation from English to Amazigh using transformer learning
Authors: Maarouf Otman , Maarouf Abdelfatah , El Ayachi Rachid , Biniz Mohamed
Published: April 2024

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.
Research and implementation of the medical text analysis algorithm for predicting mortality
Authors: Rakhmetullina Zhenisgul , Belginova Saule , Karlygash Alibekkyzy , Ismukhamedova Aigerim , Tezekpaeva Shynar
Published: April 2024

Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.
Energy efficient reliable data transmission for optimizing IoT data transmission in smart city
Authors: Desai Ruchita Ashwin , Kulkarni Raj Bhimashankar
Published: April 2024

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.
Enhancing EEG-based brain-computer interface systems through efficient machine learning classification techniques
Authors: Yassine Ferdi Ahmed , Abdelkader Ghazli
Published: April 2024

Advances in the fields of neuroscience and computer science have greatly enhanced the human brain’s ability to communicate and interact with the surrounding environment. In addition, recent steps in machine learning (ML) have increased the use of electroencephalography (EEG)-based BCIs for artificial intelligence (AI) applications. The prevailing challenge in recording EEG sensor data is that the captured signals are mixed with noise, which makes their effective use difficult. Therefore, strengthening the classification stage becomes extremely important and plays a major role in addressing this problem. In this study, we chose five most widely used classification models that obtained the best results in this field and tested them on two open-source databases. We also focused on improving the hyperparameters of each algorithm to obtain best results. Our results indicate excellent results on the first dataset and acceptable for most models on the second, while RF showed superior performance on both with an accuracy of 100% on the first dataset and 86.47% on the second. This was achieved with the lowest training costs, and better performance compared to previous works we evaluated that used the same databases. These results provide valuable insights and advance the development of brain-computer interface (BCI) technology and design.
Multilevel routing for data transmission in internet of things
Authors: Ahlawat Bhawna , Sangwan Anil
Published: April 2024

Internet of things (IoT) is the network in which sensor nodes sense information and transmit sensed information to sink. The small size and far deployment of SN results in causing problems related to higher energy utilization. Many techniques have been proposed in the last years to improve lifetime of the network. The already developed methods are the clustering techniques which are path optimization algorithms. The virtual grid-based dynamic routes adjustment (VGDRA) is the protocol which is already been proposed to increase network’s duration. The VGDRA protocol improve life span but doesn't solve the issue of energy hole which affect network performance. This work aims to improvise the VGDRA algorithm to solve the power hole problem. The utilization of cache motes is done in the network and the sink will move energy to cache nodes for the data collection. MATLAB is executed to simulate the suggested model, and amount of dead motes, active ones and amount of packets, whose transmission is done to base station (BS).