bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring the Acoustic Reflex through the Tympanic Membrane
Authors: Hamra Matan , Tetin-Schneider Simona , Shinnawi Shadi , Cohen Vaizer Mauricio , Yelin Dvir
Published: April 2024

Introduction: The acoustic reflex is the active response of the middle ear to loud sounds, altering the mechanical transfer function of the acoustic energy into the inner ear. Our goal was to observe the effect of the acoustic reflex on the tympanic membrane by identifying a significant nonlinear increase in membrane oscillations. Methods: By using interferometric spectrally encoded endoscopy, we record the membrane oscillations over time in response to a loud, 200-ms-long acoustic stimulus. Results: A gradual reflex activation is measured between approximately 40 and 100 ms, manifested as a linear 42% increase in the umbo oscillation amplitude. Conclusion: The measured oscillations correlate well with those expected from a mechanical model of a damped harmonic oscillator, and the results of this work demonstrate the potential of interferometric spectrally encoded endoscopy to observe unique dynamical processes in the tympanic membrane and in the middle ear.
A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets
Authors: Aryastana Putu , Dewi Listya , Wahyuni Putu Ika
Published: April 2024

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.
Study of the modern reclamation and ecological state of soils in Azerbaijan
Authors: Manafova Arzu Manaf
Published: April 2024

For many years in the Republic of Azerbaijan, numerous reclamation and ecological measures aimed at attracting agricultural lands of natural cenoses to use in agrocenoses were carried out. The soils of the studied territory are quite unique in terms of their soil-forming structure and ecological conditions, and they differ from other regions of the country in terms of irrigation water content. The article reflects some important results of soil and water studies conducted over the last years. Determining the qualitative composition of groundwater and irrigation water in different seasons of the year using modern methods is relevant from an environmental and reclamation point of view. Based on remote sensing and geographic information system (GIS) technologies, our research is based on the analysis of natural and anthropogenic landscape changes and changes in natural conditions as a result of agricultural development in the 20 km buffer zone along the banks of Kura and Araz rivers. As a result of the conducted research, it was determined that the rapid growth of the population has caused the reduction of the area of agro-landscapes, the salinization of the soil, the drying of the water in the water basins, the expansion of the construction areas. This has disrupted the natural balance.
Study of Babak River water quality using physical and chemical parameters in Kebon Kongok landfill (Lombok, Indonesia)
Authors: Meiliyadi Lalu Ahmad Didik , Rahman Muhammad Zainur , Zain Zahida Aliatu
Published: April 2024

The study has been conducted on the water quality level of the Babak River around Kebon Kongok landfill, Lombok. This research was conducted considering that water has many benefits in the daily lives of people. This research uses an experimental method by analyzing physical parameters (temperature, conductivity, pH, and total dissolved solids (TDS)), and chemical parameters in the form of heavy metal content lead (Pb), iron (Fe), manganese (Mn), and copper (Cu)). Analysis results were compared with quality standards based on Ministry of Health Regulation No. 32 of 2017 concerning water quality. Based on the study, it was found that the farther the distance of water measured from the source of pollution, the value of the physical and chemical parameters of water will be smaller. Most of the physical parameter test results show the Babak River water quality is still at the safe threshold limit but there is 1 point of river water that is polluted due to the distance that is too close to the active zone. Chemical parameters show the results of heavy metal content below the threshold limit. Analysis of the water quality shows that the water is still in good condition, which is below the threshold set by the Indonesian government.
An improved golden jackal optimization algorithm for combined economic emission dispatch problems
Authors: Ragunathan Ramamoorthi , Ramadoss Balamurugan
Published: April 2024

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.
CHBMLI based DSTATCOM for power quality improvemt in a three-phase three-wire distribution system with PI controller
Authors: Madhu Babu Thiruveedula , Chenchireddy Kalagotla , Sreevarsha Kama , Praveen Badudhala , Mohammad Thanveer , Kashinadh Ganji
Published: April 2024

This paper presents a cascaded h-bridge (CHB) multilevel inverter (MLI) based MLI based distribution static synchronous compensator (DSTATCOM). The main objective of this paper is to reduce source current harmonics in the distribution system by using a cascade H-bridge multilevel inverter (CHBMLI) as DSTATCOM with a proportional-integral (PI) controller. The PI controller compensates reactive power, reduces source current harmonics, and maintains the unity power factor in the distribution system. The conventional two-level inverter-based DSTATCOM has many disadvantages such as high total harmonic distortion (THD), and high switching stress power semi-conductor devices, suitable for only low-power applications. This paper to overcome these drawbacks by using MLI-based DSTATCOM. The proposed system simulations are verified in MATLAB/Simulink software. The verified results are source current, load current, and compensating current.
Novel cascaded switched-diode five level inverter for renewable energy integration
Authors: Ghughuloth Dhasharatha , Varma Nampally Rajashekar , Adwaith Donda , Hussain Shakeer , Gundoji Karthik , Samineni Harshini
Published: April 2024

This research presents the usage of a unique five-level cascaded switching diode for medium voltage integration of renewable energy sources. Its primary purpose is to decrease the quantity of gate drivers and switches. In addition to that, the cost and space for the installation of multilevel inverters are less. The inverter topology of novel cascaded multilevel inverters and switched diodes will combine both benefits. One-cycle control (OCC), which is used for clock phase-shifting (CPS) to regulate a two-stage container security device (CSD) multilevel inverter of renewable energy integration, was created to address issues with the multilevel inverter's direct current (DC) source variations. The topology also provides efficiency, harmonic distortion reduction, and voltage output quality. Waveforms are created, and a robust resistance to DC source variations is attained. The viability of the unique five-level inverter using cascaded switched diodes is confirmed by discussing the results of both simulation and experiment.
Automatic slice thickness measurement on computed tomography images of American College of Radiology phantom
Authors: Anam Choirul , Insiano Dewi A. , Hidayanto Eko , Naufal Ariij , Maya Annisa Tenri , Mulatomo Tunggul Drajat , Ali Mohd Hanafi
Published: April 2024

This study aims to develop an automatic method for calculating slice thickness on an American College of Radiology (ACR) phantom and evaluate its accuracy at variations of orientation angle and slice thickness. The phantom was scanned using Siemens SOMATOM perspective, with variations of the slice thickness (i.e. 1.5, 3, 5, 6, 7, and 10 mm) and rotation angles (i.e. 0.0, 22.5, 45.0, and 67.5°). The phantom rotation was based on the bone object as a reference. After determining the rotation angle, the phantom image was rotated by the angle. Next, profiles of pixel values across the wire objects for measuring slice thickness were developed from rotated phantom images. The slice thickness was measured automatically from the obtained profiles. The results of the automated method are 2.5, 4.1, 5.4, 5.8, 7.8, and 9.8 mm for all varied slice thicknesses. The differences between the automatic and manual methods are within 0.3 mm. The automatic method is capable of detecting slice thickness for various angles. The differences in slice thickness for various angles are within 0.1 mm for a slice thickness of 3 mm. These results are similar when compared to manual measurements. An algorithm for automated slice thickness measurement on ACR phantom has been successfully developed.
Ecological assessment of pastures semi-deserts and dry steppes of Azerbaijan
Authors: Oqtay Afat Mammadova , Nazim Roza Mammadova , Dursun Nargiz Ashurova
Published: April 2024

The absence in the scientific literature of criteria for assessing the ecological status of pasture lands, insufficient knowledge of the use of predictive methods and technology for carrying out special agrochemical measures, as well as issues of permissible loads, served as the basis for choosing the topic of research work. For the first time, in the conditions of pastures in Azerbaijan, an environmental and energy assessment of soil-landscape complexes was carried out. A detailed and final quality assessment was drawn up on soil scale, and the coefficient of their comparative merit was determined. A scientifically based system of agrochemical measures for the superficial and fundamental improvement of pastures has been developed. The final bonitet scale, reflecting the level of fertility of soil varieties, showed that the soils of the Jeyranchol massif turned out to be the most fertile at 62 points, compared with them, the pasture soils of Ajinohur on average across the massif received 53 points, Gobustan 51 points, and the Kura-Araz lowland 55 points. On average, pasture lands in Azerbaijan are valued at 55 points, which indicates the need for agro-reclamation measures. The types of forage plants are distributed as follows: i) Cereals 116, 12%; ii) Asteraceae 109, 11.2%; iii) Legumes 82, 8.4%; iv) Brassicas 59, 6.2%; 6%; v) Cloves 50, 5.3%; vi) Lamiaceae 42, 4.6%; vii) Linear 40, 4.2%; viii) Goosefoot 6, ix) Gimletaceae 32 species, 3.4%; x) Umbrellas 38, 4.4%; xi) Other 339 types, 35.5%.
Fundamental frequency switching strategies of a seven level hybrid cascaded H-bridge multilevel inverter
Authors: Chenchireddy Kalagotla , Sreejyothi Khammampati R. , Ganesh Podishetti , Kiran Gatla Uday , Shiva Chilukuri , Kumar Banoth Nithish
Published: April 2024

This paper presents a novel hybrid cascaded H-bridge multilevel inverter (HCHB MLI) designed to address the growing importance of multilevel inverters in the context of renewable energy sources such as solar, wind, and fuel cells. The proposed topology features eight insulated-gate bipolar transistor (IGBT) switches and utilizes two distinct input direct current (DC) sources: a battery and a capacitor, making it a hybrid system. The control strategy employed in this topology is based on fundamental switching frequency techniques. Simulation results of the proposed topology are conducted using MATLAB/Simulink software, while hardware experimentation with a single-phase H-bridge inverter is also demonstrated in the paper. For pulse generation and IGBT switch control, an Arduino UNO microcontroller is utilized. The output voltage of the single-phase H-bridge inverter is verified through experimentation using a cathode-ray oscilloscope (CRO).
Performance evaluation of solar-PV integrated hybrid fuzzy-logic controlled multi-functional UPQC for enhancing PQ features
Authors: Rao Lingineni Shanmukha , Mogilicharla Veera Narasimha Murthy , Sharma Pidatala Prabhakara , Rajkumar Prathipati
Published: April 2024

To improve distribution system voltage and current quality, a newly built solar-PV system connected multi-functional universal power quality compensator (MFUPQC) has been extensively used. The proposed MFUPQC mitigates both load and source-side concerns in a three-phase distribution system. Furthermore, as part of the distributed generation scheme, active power from solar PV is injected into the grid or source when solar PV is available. In this context, the proposed MFUPQC was tested in both PQ enhancement and DG integration modes using a feasible control scheme. The proportional-integral controller is used for shunt- voltage-source inverter (VSI) DC-link control, which is not suitable for regulating DC-link voltage at the desired level due to incorrect gain value selection. In this work, an intelligent hybrid-fuzzy-logic DC-link control of MFUPQC evidences the intelligent knowledge base for better regulation of power-quality issues. The suggested hybrid fuzzy-logic controlled MFUPQC device's performance for both power quality (PQ) improvement and DG integration is validated using the MATLAB/Simulink software tool, and simulation results are provided with an appealing comparison analysis.
Cases of PD-CFPID selector for frequency adjustment of integrated power grid
Authors: Sahoo Ananta Kumar , Nayak Smrutiranjan , Sahoo Ashwin Kumar , Dash Subhransu Sekhar , Kar Sanjeeb Kumar
Published: April 2024

In current article an improved salp swarm algorithm (SSA) has been suggested. Likewise, the adjustable scaling-component is locked in to manage salp’s situation in time of the hunt system to limit arbitrary development salps. The upgraded salp swarm method combines random objection-based learning, multiple management, and simulated hardening in swarm brilliant-based metaheuristic salp swarm method, which rises the exploration and exploitation of the primary salp swarm method. To exhibit viability of the improved SSA, a bunch of multichannel test capabilities are locked in. A partially decentralized combined fuzzy proportional integral derivative (PD-CFPID) regulator is planned for integrated grid. A partially decentralized robust control plan is introduced to deal with a class of multi-inputs multi outputs (MIMO) non-square, linear framework different systems. Partially decentralized command is a control composition that lies between a completely decentralized structure and a wholly centralized one, and has the primacy of achieving comparable performance as an entirely centralized regulator but with simpler shape. The proposed flexible partially decoupled command scheme works in a straightforward and systematic way. Simulations show that the proposed partially decentralized control performs well for the nonlinear system, nearly equivalent to the multivariable control, and they achieve better performance indexes. Simulation outcomes appear that the stated method is simple and can reach better performance. The results of the PD-CFPID regulator are compare among CFPID and PID as many circumstances of the presented control outlook.
Combining solar panels with plants for sustainable energy and food production: state of the art
Authors: Panda Sampurna , Kumar Rakesh , Panda Babita , Panda Bhagabat , Raj Ashish
Published: April 2024

The need for alternative energy sources becomes extensive because of the escalating cost of fossil fuels. The goal of this paper is to examine the effectiveness of combining photovoltaics and agriculture for better yield. Photovoltaic (PV) solar plants will compete with farms for available land. In this study, the methodologies are discussed how it is possible to maximize land utilization by placing solar arrays and food crops on the same plot of land. The term is proposed "agrivoltaic system" to describe this setup. Conventional solutions (discrimination of agricultural and energy extracting) were compared to two agrivoltaic schemes with varying density of PV arrays using land equivalent ratios. We utilized a crop model to simulate the amount of sunlight reaching the crop from an array of solar panels and to speculate on the yield reduction that would result from the partial shading. These early findings suggest that agrivoltaic systems may be highly effective; the two densities of PV panels were anticipated to boost worldwide land production by 73%. One possible explanation for the success of these hybrid systems is the presence of facilitation mechanisms analogous to those seen in agroforestry. At the end it is suggested that in places where arable land is rare, new solar plants may find it beneficial to produce both power and food.
A regulatory power split strategy for energy management with battery and ultracapacitor
Authors: Mohanan Sreekala Vazhakkuzhackal , Vijayan Abhilash Theckevalel
Published: April 2024

Electric vehicle batteries face fast degradation due to the high frequency of charging/discharging cycles and great peak power demands. Lifetime, continuity of supply and power density of these batteries affect the performance of electric vehicles (EVs). Hybrid energy storage systems (HESS) offers a feasible solution by incorporating other energy storage elements like ultra-capacitor (UC) along with battery. Their combination provides higher efficiency and better performance in terms energy/power density. UC can behave like a power buffer when the EV is accelerating and regenerating. The HESS needs a controller that can split the available power between different sub systems as per demand. This paper presents a regulatory control strategy useful in HESS with battery and UC for the speed regulation of a brushless DC (BLDC) motor using a 3-port bidirectional DC-DC converter. The regulatory control strategy monitors the state of charge (SOC) of UC and a fuzzy logic controller regulates the power flow between HESS and the motor. Simulation in MATLAB validates the efficacy of the strategy. Simulation results and hardware evaluation confirm that the regulatory control scheme is effective in splitting the available power according to the load demand and achieves better energy efficiency.
A wireless-power and data-transfer using inductive RF link and ASK modulation
Authors: Rao K. V. S. Subramanyeswara , Rao G. Raja , Vaisakh Kanchapogu
Published: April 2024

This paper aims to present a novel trans-cutanaeous, wireless and an efficient radio frequency (RF) inductive power and data link (IPDL) based on amplitude shift key (ASK) - modulator with an efficient ‘Class-E’– RF ‘power amplifier’ that is applicable to implantable medical devices (IMDs). The IPDL system comprises an external device placed on exterior to human skin to transmitting power and data to IMDs like implantable micro-systems (cardiac pacemakers, cochlear implants, retinal implants, deep brain stimulators) to excite and monitor respective neural and muscular system. The entire system is designed to operate with a ‘low-band-frequency’ of 4 MHz by avoiding the problem of tissue heating. A practical model for the IPDL with results is presented. Various graphs such as frequency vs primary coil/secondary coil output are plotted and shown. Typical specifications of the exterior device, namely, the modulation-index (MI) and the modulation-rate (MR) are 40% and 4.3% respectively with a data rate of 172 Kbps. The design is simulated with electronic workbench MULTISIM 12.0 and TINA Ver 9.
Comprehensive review and analysis of photovoltaic energy conversion topologies
Authors: Mansouri Adil , El Magri Abdelmounime , Younes El Khlifi , Lajouad Rachid , Adouairi Mohamed Said
Published: April 2024

Energy conversion is a pivotal process with widespread applications, spanning renewable energy systems, electric vehicles, and industrial power grids. Selecting the right energy conversion topology is critical for optimizing system performance, efficiency, and reliability. This comprehensive review paper provides a thorough overview of energy conversion topologies used in photovoltaic (PV) panel systems, as well as their applicability in diverse domains. Furthermore, the paper conducts a detailed analysis of commonly employed energy conversion topologies. Each topology is meticulously examined based on its operating principles, advantages, drawbacks, and typical use cases. This comprehensive review serves as an invaluable resource for researchers, engineers, and practitioners engaged in the dynamic field of energy conversion, offering insights into both wind energy and photovoltaic panel systems.
An improved modulation technique suitable for a three level flying capacitor multilevel inverter
Authors: Mahafzah Khaled A. , Negry Raneem M. , Obeidat Mohammad A. , Alsalem Hesham
Published: April 2024

This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed simplified modulation technique paves the way for more straightforward and efficient control of multilevel inverters, enabling their widespread adoption and integration into modern power electronic systems. Through the amalgamation of sinusoidal pulse width modulation (SPWM) with a high-frequency square wave pulse, this controlling technique attains energy equilibrium across the coupling capacitor. The modulation scheme incorporates a simplified switching pattern and a decreased count of voltage references, thereby simplifying the control algorithm.
Neural network optimizer of proportional-integral-differential controller parameters
Authors: Siddikov Isamiddin , Nashvandova Gulrukhsor , Alimova Gulchekhra
Published: April 2024

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches
Authors: Elgandelwar Sachin M. , Bairagi Vinayak , S. Vasekar Sridevi , Nanthaamornphong Aziz , Tupe-Waghmare Priyanka
Published: April 2024

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.
Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration
Authors: Julian James , Wahyuni Fitri , Dewantara Annastya Bagas , Winarta Adi , Putra Nandy
Published: April 2024

Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices.
Medication dispenser with touch screen and consumption authenticator for monitoring adherence to medication in the elderly
Authors: Linder Rubiños Jimenez Santiago , Adolfo Vidal Sánchez Ricardo Sergio , Enrique Solis Farfán Roberto , Alfredo Vallejos Zuta Alex , Junior Grados Espinoza Herbert , Linett Velasquez Jimenez Angélica
Published: April 2024

The elderly population continues to grow, this population has many chronic conditions, which is why they take multiple pills at the same time, this situation makes it difficult for them to take their medications at the appropriate time, which can cause health complications and even death. death. The objective of this research is to improve medication adherence using a medication dispenser that allows authenticating medication consumption accurately with a high level of usability. To do this, we implemented a dispenser with an alarm system that can be configured from a graphical interface, with internet of things (IoT) to remotely monitor the intake of pills and authenticate their consumption through artificial vision that will use the instant messaging system to inform the caregiver about the situation and finally measure the dispenser usability.
An improved mining image segmentation with K-Means and morphology using drone dataset
Authors: Haqiq Nasreddine , Zaim Mounia , Sbihi Mohamed , El Alaoui Mustapha , Masmoudi Lhoussaine , Echarrafi Hamza
Published: April 2024

The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.
Experimental analysis for comparison of wireless transmission technologies: Wi-Fi, Bluetooth, ZigBee and LoRa for mobile multi-robot in hostile sites
Authors: Abderrahmane Tamali , Nourredine Amardjia , Mohammed Tamali
Published: April 2024

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-to-noise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments.
Framework for multiple person identification using YOLOv8 detector: a transfer learning approach
Authors: Jayaram Dileep , Vedagiri Supriya , Ramachandra Manjunath
Published: April 2024

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.
Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases
Authors: Muniappan Ramaraj , Thangavel Thiruvenkadam , Manivasagam Govindaraj , Sabareeswaran Dhendapani , Thangarasu Nainan , Jothish Chembath , Ilango Bhaarathi
Published: April 2024

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.
Intelligent intrusion detection through deep autoencoder and stacked long short-term memory
Authors: Moukhafi Mehdi , Tantaoui Mouad , Chana Idriss , Bouazi Aziz
Published: April 2024

In the realm of network intrusion detection, the escalating complexity and diversity of cyber threats necessitate innovative approaches to enhance detection accuracy. This study introduces an integrated solution leveraging deep learning techniques for improved intrusion detection. The proposed framework consists on a deep autoencoder for feature extraction, and a stacked long short-term memory (LSTM) network ensemble for classification. The deep autoencoder compresses raw network data, extracting salient features and mitigating noise. Subsequently, the stacked LSTM ensemble captures intricate temporal dependencies, correcting anomaly detection precision. Experiments conducted on the UNSW-NB15 dataset, and a benchmark in intrusion detection validate the effectiveness of the approach. The solution achieves an accuracy of 90.59%, with precision, recall, and F1-Score metrics reaching 90.65, 90.59, and 90.57, respectively. Notably, the framework outperforms standalone models and demonstrates the advantage of synergizing deep autoencoder-driven feature extraction with the stacked LSTM ensemble. Furthermore, a binary classification experiment attains an accuracy of about 90.59%, surpassing the multiclass classification and affirming the model's potential for binary threat identification. Comparative analyses highlight the pivotal role of feature extraction, while experimentation illustrates the enhancement achieved by incorporating the synergistic deep autoencoder-Stacked LSTM approach.
Cryptocurrency fraud detection through classification techniques
Authors: Tripathy Nrusingha , Kumar Balabantaray Sidhanta , Parida Surabi , Nayak Subrat Kumar
Published: April 2024

Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.
Fine-grained hate speech detection in Arabic using transformer-based models
Authors: Bensoltane Rajae , Zaki Taher
Published: April 2024

With the proliferation of social media platforms, characterized by features such as anonymity, user-friendly access, and the facilitation of online community building and discourse, the matter of detecting and monitoring hate speech has emerged as an increasingly formidable challenge for society, individuals, and researchers. Despite the crucial importance of hate speech detection task, the majority of work in this field has been conducted in English, with insufficient focus on other languages, particularly Arabic. Furthermore, most existing studies on Arabic hate speech detection have addressed this task as a binary classification problem, which is unreliable. Therefore, the aim of this study is to provide an enhanced model for detecting fine-grained hate speech in Arabic. To this end, three transformer-based models were evaluated to generate contextualized word embeddings from input sequence. Additionally, these models were combined with a bidirectional gated recurrent unit (BiGRU) layer to further improve the extracted semantic and context features. The experiments were conducted on an Arabic reference dataset provided by the open-source Arabic corpora and processing tools (OSACT-5) shared task. A comparative analysis indicates the efficiency of the proposed model over the baseline and related work models by achieving a macro F1-score of 61.68%.
k-dStHash tree for indexing big spatio-temporal datasets
Authors: Hooda Meenakshi , Gill Sumeet
Published: April 2024

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.
Real-time phishing detection using deep learning methods by extensions
Authors: Minh Linh Dam , Hung Ha Duy , Minh Chau Han , Sy Vu Quang , Tran Thanh-Nam
Published: April 2024

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.
A novel and optimized computational framework for energy efficient data dissemination in wireless sensor network
Authors: Mathew Kavanathottahil Deepa , Anita Jones Mary Pushpa Thomas
Published: April 2024

Wireless sensor network (WSN) is an integral part of internet-of-things (IoT), where a large scale of data transmission and various complex services are identified to be delivered. In order to facilitate these services, energy efficiency is one critical demand for resource-constraint sensor nodes. Carrier sense multiple access (CSMA) has been considered for effective traffic management for high-end data delivery services. A review of existing literature on CSMA-based schemes shows that it has not yet achieved an optimal case of energy efficiency. Hence, the proposed study presents a novel computational framework in order to address this research gap. The prime contribution of the proposed study is towards presenting an optimal computational model for maximized fairness in data dissemination services in WSN especially focusing on energy efficiency. The presented study model also contributes towards optimizing route buffer and buffer power, which facilitates towards availability of energy-efficient path information. The study also introduces a mobile auxiliary node that aggregates the data from sensor nodes and delivers it to the sink node considering dynamic location updates for seamless transmission. Scripted in MATLAB, the proposed scheme exhibited 70% energy saving compared to conventional schemes of CSMA in WSN.
Approach to automating the construction and completion of ontologies in a scientific subject field
Authors: Sadirmekova Zhanna , Murzakhmetov Aslanbek , Abduvalova Ainur , Altynbekova Zhanar , Makhatova Valentina , Akhmetzhanova Shynar , Tasbolatuly Nurbolat , Serikbayeva Sandugash
Published: April 2024

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.
Coffee bean graded based on deep net models
Authors: Balakrishnan Jayakumari Bipin Nair , Koovamoola Mambilamthoda Abrav Nanda , Stephen Shalwin Ambalamoottil , Venkitesan Pranav , Raghavendra Venkatesh
Published: April 2024

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of high-resolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(Resnet34), inception version 3 (Inception v3), efficient network bayesian optimization (EfficientNet-B0), and visual geometry group-16(VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions.
Deep learning and quantization for accurate and efficient multi-target radar inference of moving targets
Authors: Ernest Mashanda Nyasha , Watson Neil , Berndt Robert , Abdul Gaffar Mohammed Yunus
Published: April 2024

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify ground-moving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints.