bluuebunny/crossref_metadata_2025 huggingface dataset:
Diffuse Alveolar Hemorrhage Associated with Epithelioid Angiosarcoma of the Lung: A Challenging Diagnosis
Authors: Wuest Felix , Gebhardt Andreas , Grosswendt Claudia , Griff Sergej , Zhilina Antonina , Taber Samantha , Kollmeier Jens , Bauer Torsten T.
Published: June 2023

A 68-year-old patient presented with persistent hemoptysis and weight loss. A CT scan showing diffuse bilateral ground-glass opacities and nodules was followed by bronchoscopy. While diffuse alveolar hemorrhage (DAH) could be seen, specimens obtained during bronchoscopy did not provide conclusive histological findings. The decision was made to conduct video-assisted wedge resection, after which histological examinations revealed the diagnosis of bifocal nodular manifestation of an epithelioid angiosarcoma in the lung. Being a rare entity even among sarcomas, these kinds of tumors can be primary lung tissue angiosarcomas or metastatic lesions with primaries in places like the skin, breast, and heart. Treatment usually includes chemotherapy, but prognosis remains grim. This case highlights that in DAH, uncommon causes should be considered, and sufficient probe gathering is the key to early diagnosis and treatment.
Improvement of Antioxidant Defences in Keratinocytes Grown in Physioxia: Comparison of 2D and 3D Models
Authors: Chettouh-Hammas Nadira , Fasani Fabienne , Boileau Amandine , Gosset David , Busco Giovanni , Grillon Catherine
Published: June 2023

Keratinocytes prevent skin photoaging by ensuring the defence against oxidative stress, an excessive production of reactive oxygen species (ROS). They are localized within the epidermis where the oxygen level (1-3% O2), named physioxia, is low compared to other organs. Oxygen is essential for life but also generates ROS. Most of the in vitro studies on keratinocyte antioxidant capacities are performed under atmospheric oxygen, named normoxia, which is very far from the physiological microenvironment, thus submitting cells to an overoxygenation. The present study is aimed at investigating the antioxidant status of keratinocyte grown under physioxia in both 2D and 3D models. First, we show that the basal antioxidant profiles of keratinocytes display important differences when comparing the HaCaT cell line, primary keratinocytes (NHEK), reconstructed epidermis (RHE), and skin explants. Physioxia was shown to promote a strong proliferation of keratinocytes in monolayers and in RHE, resulting in a thinner epidermis likely due to a slowdown in cell differentiation. Interestingly, cells in physioxia exhibited a lower ROS production upon stress, suggesting a better protection against oxidative stress. To understand this effect, we studied the antioxidant enzymes and reported a lower or equivalent level of mRNA for all enzymes in physioxia conditions compared to normoxia, but a higher activity for catalase and superoxide dismutases, whatever the culture model. The unchanged catalase amount, in NHEK and RHE, suggests an overactivation of the enzyme in physioxia, whereas the higher amount of SOD2 can explain the strong activity. Taken together, our results demonstrate the role of oxygen in the regulation of the antioxidant defences in keratinocytes, topic of particular importance for studying skin aging. Additionally, the present work points out the interest of the choice of both the keratinocyte culture model and the oxygen level to be as close as possible to the in situ skin.
Summarizing twitter posts regarding COVID-19 based on n-grams
Authors: Alabid Noralhuda N. , Naseer Zahraa
Published: June 2023

The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the most tweets to understand what was firstly discussed about the related topic. Some strategies were developed for summarizing tweets but summarizing topics of COVID-19 are still at the beginning. The current research aims to introduce a technique to present a short summary related COVID-19 topics with consuming little time and effort. Thus, summarization task started by clustering topics based on latent dirichlet allocation (LDA) method and K-means clustering and then selected the important sentences to format summarization. The study also compares bigram-based and unigram-based summarization. Different metrics were used to evaluate results and experiments at each stage, and the output of the proposal system was evaluated using ROUGE metrics.
Machine learning based approach for detection of fake banknotes using support vector machine
Authors: Easa Haider Khalil , Saber Ali Ali , Hamid Noor Kaylan , Saber Hindren Ali
Published: June 2023

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our experiment will demonstrate that the suggested algorithm does really yield extremely good performance.
Efficient and secure hybrid chaotic key generation for light encryption device block cipher
Authors: Al-Saadi Hussain M. , Alshawi Imad S.
Published: June 2023

Lightweight cryptographic algorithms must develop to ensure the confidentiality and integrity of the data in resource-constrained devices. Keys are vital to every cryptography algorithm because they provide randomness, complexity, unexpected nature, and robustness. A light encryption device (LED) is considered a lighter version of advanced encryption standard (AES), but it is vulnerable to related key attacks due to using the same key during the whole encryption process. This paper presents a hybrid chaotic key generator (HCKG) based on 3D Lorenz, and 2D Henon maps to generate a highly randomized key that combines with the LED to provide a high level of secure encryption on resource-constrained devices. We modified the HCKG every four rounds via simple operations to get the subkeys and XORed it with the state to increase the complexity of the ciphertext. Moreover, the HCKG with subkeys allows us to decrease the total number of LED rounds from 32 to 24 to minimize the calculation cost while maintaining a high level of security. National Institute of Science and Technology (NIST) test suite proves that the proposed LED-HCKG demonstrates a high-performance increase by nearly 0.3283 higher than LED concerning data integrity and secrecy.
Associating deep learning and the news headlines sentiment for Bursa stock price prediction
Authors: Sidek Zuleaizal , Ahmad Sharifah Sakinah Syed , Teo Noor Hasimah Ibrahim
Published: June 2023

Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysia between January 2017 and April 2022, and the root mean squared error (RMSE) value was calculated. In addition, LSTM prediction model was compared to the decision tree algorithm, and LSTM performed significantly better than the decision tree, particularly when using the New York stock exchange (NYSE) dataset. Furthermore, sentiment analysis was carried out using a Malaysian online news portal's business and financial news. The findings showed that i) news has a significant impact on Malaysian stock market price movement; ii) the RMSE of the LSTM model was improved by adding a parameter (news polarity values); and iii) the RMSE value generated is less than one for every company stock and is influenced by stock price and price change magnitude.
A multi-instance learning based approach for whitefly pest detection
Authors: Chand Lal , Dhiman Amardeep Singh , Singh Sikander
Published: June 2023

Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents four different approaches for automated classification of whiteflies which are the Bayesian network, convolution neural network (CNN), ResNet and multi-instance learning-CNN. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed technique has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 95.53%, 96.9%, 97.6% and 98.13% for the four models respectively. A comparative analysis in terms of accuracy of classificaiton, with existing techniques shows that the proposed technique outperforms baseline deep learning models identifying whitefly infestation.
Novel extraction and tracking method used in multiplelevel for computer vision
Authors: Ghindawi Ekhlas Watan , Abdulateef Sally Ali
Published: June 2023

Various scientific applications, including cosmological simulation, fluid simulation, and molecular dynamics, depend heavily on the analysis of particle data. Although there are techniques for feature extraction and tracking regarding volumetric data, it is more difficult to do such tasks for particle data due to the lack of explicit connectivity information. Even though one could transform the particle data to volume beforehand, doing so runs the risk of incurring error and growing the data size. In order to facilitate feature extraction and tracking for scientific particle data, we adopt a deep learning (DL) method in this research. In order to capture the relation between physical features and spatial locations in a neighborhood, we use a DL model that generates latent vectors. Through clustering the latent vectors, characteristics could be retrieved from the vectors. The Cam-shift tracking algorithm, which just needs inference of the latent vector for chosen regions of interest, is implemented in the feature space to accomplish quick feature tracking. With the use of two datasets, we test our approach and contrast it with other approaches already in use.
Time series prediction of personalized insulin dosage for type 2 diabetics
Authors: G. Jisha , Bhuvan Nikhila T. , Jerrard Ritta
Published: June 2023

Careful blood glucose monitoring and consistent insulin administration are necessary for managing diabetes. People with demanding schedules or little access to medical personnel may find this difficult. Fortunately, without having to visit a doctor every day, daily insulin dosage may now be customized to a person’s unique needs using technology and customised algorithms based on their food intake, exercise routines, and blood glucose levels. This information can be entered into a diabetes management app or device, where an algorithm will determine the proper insulin dosage and offer real-time feedback to assist maintain ideal blood glucose levels. A patient's dietary preferences, degree of physical activity, and blood sugar are taken into account for determining the proper bolus and basal insulin dosages in this study. According to the tracked body data, a patient’s appropriate insulin dosage is predicted using artificial neural network (ANN)-based models. Based on patient activity, food intake, exercise, and past insulin administration, insulin projections are created. To forecast an individual’s basal and bolus insulin requirements, long short-term memory (LSTM) and random forest regression models are employed. Accuracy of both models are tested and random forest regression shows better accuracy which is used in the prediction system.
Cybersecurity in health sector: a systematic review of the literature
Authors: Herrera Catherine Vanessa Peve , Valcarcel Jonathan Steve Mendoza , Díaz Mónica , Salazar Jose Luis Herrera , Andrade-Arenas Laberiano
Published: June 2023

Currently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors.
An improved clustering based on K-means for hotspots data
Authors: Muhima Rani Rotul , Kurniawan Muchamad , Wardhana Septiyawan Rosetya , Yudhana Anton , Sunardi Sunardi , Adhimukti Mitra
Published: June 2023

Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster. Experimentally compared the performance of GAP K-means, GA K-means, and K-means on the hotspots data, two artificial datasets, and three real-life datasets. Sum square error (SSE), davies bouldin index (DBI), silhouette coefficient (SC) and F-measure are used to evaluation clustering. Based this experiment, GAP K-means outperforms than K-means but GAP K-means still not fast to achieve convergent than GA K-means.
Nexus between Iraqi SMEs cloud computing adoption intention and firm performance: moderating role of risk factors
Authors: Alasady Ali Salah , Hashim Hayder Salah , Awadh Wid Akeel
Published: June 2023

Cloud computing has been adopted in many developing nations, namely Iraq and Ghana, for its multiple benefits. Nonetheless, there is a dearth of studies on cloud computing adoption in developing nations. This study aims to investigate the factors influencing cloud computing adoption among Iraqi small and medium-sized enterprises (SMEs) and the implication of cloud computing on the SME firm performance. The diffusion of innovation (DOI) theory served as the foundation for the research model. Primary data from 396 SMEs were analysed using the structural equation modelling (SEM) approach through SmartPls 3.0 software for model testing. Resultantly, the use of cloud computing enhances firm performance. The technological factor also positively affects firm performance and cloud computing. The findings provide cloud service providers, managers, and government regulators with important recommendations to encourage the adoption and effective implementation of cloud computing in Iraq.
An adaptive system for predicting student attentiveness in online classrooms
Authors: Gupta Bhavna , Sharma Richa , Bansal Roli , Soni Gagan Kumar , Negi Parul , Purdhani Paawan
Published: June 2023

The effectiveness of teaching methods is determined by whether a student is attentive in a lecture or not. In face-to-face classroom teaching, a teacher is able to judge whether students are understanding the subject, based on their facial expressions. However, since the uprise of COVID-19 pandemic, virtual classrooms have found a holding in the field of education and detecting attentiveness of students is a challenge in the same. This paper proposes a student attentiveness model that would detect and monitor a student’s eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher. The proposed model employs a histogram of oriented gradient (HOG) method in conjunction with support vector machine (SVM) algorithm for face recognition. It then computes an adaptive eye aspect ratio (AEAR) for each individual student to determine their level of attentiveness. The model is tested on a real-time dataset and validated using classifiers (SVM, decision tree, and random forest). The results of the classifiers verify that the model produces an accuracy of more than 92%.
Electronic document management system for local area network-based organizations
Authors: Gamido Marlon V. , Gamido Heidilyn V. , Macaspac Daryl James P.
Published: June 2023

The paper discusses the design and development of an electronic document management system (EDMS) that prepares documents for sharing and information dissemination. EDMS has to do with capturing, storing, indexing, retrieval, and disposal of documents. The electronic document management system process starts by converting paper document into digital record to efficiently store and organize document in standardized file structure and format, promoting a paper waste reduction in reproducing the document. The EDMS provided an easier way of sharing information with different stakeholders and securing documents according to standardized compliance rules. The developed system has also improved the accessing of vital documents by users since there is real-time information distribution of information and easy searching and retrieving of needed documents.
Validation of the toolkit for fake news awareness in social media
Authors: A. Al-Shareeda Mahmood , Saare Murtaja Ali , Manickam Selvakumar , Karuppayah Shankar
Published: June 2023

Fake news has gained attention in recent years, particularly among social media users. The quick spread of fake news has been made possible by the increased usage of social media as a platform to get the most recent news and information. As a consequence, it is getting harder to distinguish between real news and fake news. This essay will outline a study that evaluated the usefulness of a toolset for identifying false news. The first hypothesis is that an increase in media literacy will result in a rise in awareness of fake news. The second theory is that fake news toolkits significantly increase students’ and working adults’ awareness of false news. On the staff of a manufacturing company and an institution, a survey was undertaken. The organization’s employees were given approximately 150 questionnaires, and 110 responses were received. The project includes the creation of a web application-a false news awareness toolkit-that will raise user awareness of fake news in terms of knowledge, behaviour, and attitude among students and workers.
Wireless sensor networks in the internet of things: review, techniques, challenges, and future directions
Authors: Zijie Fu , Al-Shareeda Mahmood A. , Saare Murtaja Ali , Manickam Selvakumar , Karuppayah Shankar
Published: June 2023

Wireless sensor networks (WSN) are an emerging multidisciplinary intersection of cutting-edge research fields, and their advantages in terms of freedom of formation, high signal-to-noise ratio, high strength, and unattended, which makes WSN have good prospects for application in the field of internet of things (IoT). Considering all the benefits that WSN offer, this paper reviews the development history of wireless sensor networks internet of things (WSN-IoT), analyses the technologies used by sensors in the IoT, and illustrates the future developing patterns and remaining challenges, in conjunction with the main technologies in the perception layer of the current network of things industry.
Impact of cell temperature on the performance of a rooftop photovoltaic system of 2.56 kWp at Universitas Pamulang
Authors: Abdul Rozak Ojak , Zamri Ibrahim Mohd , Zalani Daud Muhamad , Bakhri Syaiful , Muwaffiq Rifqi
Published: June 2023

The performance of solar panels greatly determines the electrical energy production of a solar power generation system. The decrease in performance has an impact on efficiency, output power, output voltage and current. Currently, at Universitas Pamulang a solar photovoltaic system (PV) is installed with a capacity of 2.56 kWp since 2018. However, no performance test and analysis have been conducted to determine its level of efficiency and reliability. This paper presents an experimental method used for performance testing of a 320 W mono-crystalline solar panel, measuring from 08.00 AM to 4.00 PM, using the solar survey 200R to measure solar irradiation, ambient and cell temperature. A digital multimeter CD800a was used to measure Voc and a PV200 tester used to measure voltage and current output. The results revealed that at an ambient temperature of 38°C and cell temperature 50.9°C, the intensity of solar radiation was 702.7 W/m2 and output voltage of 42.9 V with a performance of 78.37% and an efficiency of 27.73% was due to an increase cell temperature. Low-efficiency values with high cell temperatures indicate that this system requires an external solar panel cooling system.
Optimum yearly and seasonal tilt angle of solar system in the center of Babylon/Iraq using PVsyst software
Authors: Aljanabi Mohanad , Salah Sadiq Al-Kafaji Muhammed , Hussein Duhis Ahmed
Published: June 2023

The photovoltaic system tilt angle is one of the more significant factors for obtaining the maximum solar energy that will fall on the PV panel. Consequently, then obtain maximum power output, the solar array needs to be angled properly. The analysis for a stand-alone system is simulated and modelled using PVsyst software version-7.2 for system power 3,120 Wp to obtain the optimum value of the tilt angles of maximum solar irradiation in the center of Babylon Governorate, Iraq. The ideal tilt angle for the south has been found for both yearly and the seasons. Different tilt angles were taken which were (29°, 30°, and 31°) annually, azimuth angle for all is (0°). The incident global irradiation in the collector plane, Incident beam irradiation in the collector plane is maximum at tilt angle (29°) that produces maximum available energy 5,132 kWh/kWp/year. same processing for annual different tilt angle was taken for seasonally; the results show that the angle (49°) for summer and (13°) for winter are the optimum tilt angle which obtains the maximum incident global irradiation in the collector plane, maximum incident beam irradiation in the collector plane at this angle then product maximum available energy 5,343 kWh/kWp/year.
Speed control of switched reluctance motors based on fuzzy logic controller and MATLAB/Simulink
Authors: Salim Hameed Hayder , Al Azze Qasim , Saadi Hasan Mohammed
Published: June 2023

Switched reluctance motors (SRM) becomes increasingly popular due to its simple structure and dependability. It seems to have a two-pole configuration. A stator had also centralized coils along each pole, whereas a rotor has an iron core only. As a result, the switched reluctance motor is anticipated to be a low cost, incredibly robust variable-speed machine. Because the switched reluctance motor is only made up of laminated coils, its performance is heavily reliant on the magnetic properties of the core material. Vibration, torque ripple, and low torque for each unit volume are some of their drawbacks. Typically, a standard proportional-integral-derivative (PID) controller is designed to regulate the Switched Reluctance Motor's speed. Furthermore, the PID controller could also regulate and control SRM when the load changes abruptly. A fuzzy logic controller (FLC) is utilized within this article to regulate the speed of the SRM drive. The MATLAB/Simulink platform is used to analyze the behavior of SRM with FLC. To demonstrate FLC's superiority, its results have been compared to that of a typical PID controller.
Design and development of biogas electrical power generation with an adaptive control mechanism for rural electrification
Authors: Kumar Pothera Kariyat Vineeth , Jisha Janardhanan Jijesh
Published: June 2023

The primary goal of this research is to implement a model of biogas power generation unit used for rural electrification. A biogas reactor, a microturbine, a permanent magnet synchronous generator (PMSG), a rectifier, and a permanent magnet DC motor (PMDC) make up the proposed system. The biomass used to fuel the biogas reactor is typically derived from the surplus agricultural and animal waste found in rural areas. The operation of the biogas power system, design, and detailed mathematical modeling of each segment is described in this paper. In addition to that, an adaptive control mechanism is added to improve the system stability. The MATLAB/Simulink environment is used to develop the overall biogas fueled power system and thus obtained various parameters. The observation in the development has shown an improvement in biogas power generation unit’s suitability for standalone applications such as water pumping in rural area.
Parameterized SDRAM-based content-addressable memory on field programmable gate array
Authors: Dang Binh , Bui Minh , Trinh Vu Dang Nguyen , Tran Linh
Published: June 2023

Contents-addressable memory (CAM) is a special memory that searches the input data with the entire pre-loaded database and generates corresponding address information. CAMs are advancing to be a core technology in computer networking systems. As field programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate CAM on FPGA is increasing. FPGA-based CAMs are divided into three categories of implementation: register-based, block RAM (BRAM)-based, and distributed RAM-based CAM. However, they come with a cost of excessive resource usage. Besides, the collision ratio is high in FPGA-based CAMs, leading to data loss and failure to produce accurate addresses. Synchronous dynamic random-access memory (SDRAM)-based CAMs, benefiting from the features of high density and low price of SDRAM, solve the limitations of FPGA’s on-chip resources. This paper proposes a data collision CAM hardware implementation using modern FPGA’s off-chip SDRAM for data storage. The hardware architecture is customized for massive lookup tables and resource-saving. Furthermore, the architecture is parameterized, which is better for integration. The synthesis results and comparisons show significant advancement compared to other FPGA-based CAM implementations by total reduction of on-chip RAM. The novel architecture shows remarkable improvement in the memory depth and width with the capacity of 128 Mbyte lookup table.
Preparation of silver sulphide (Ag2S) thin films by chemical bath deposition for photocatalytic application
Authors: Hussain Nedal A. , Latif Lamyaa A. , J. Mohamad Haidar
Published: June 2023

Semiconductor silver sulphide (Ag2S) thin films were grown on the glass substrate by chemical bath deposition (CBD). Five films deposited at the room temperature in a bath containing an aqueous solution of silver sulphide. The chemically synthesized Ag2S films are annealed at 573K for one hour for the second sample, and two hours for the third sample. The fourth sample exposed to microwave irradiation for one hour, and two hours for the fifth sample. Surface morphology, photocatalytic, optical and structural properties of all Ag2S samples investigated. The optical parameters such as transmittance, absorbance, absorption coefficient and energy bandgap of the films with thermal annealing and expose microwave irradiation presented. Optical measurement shows high absorbance in the ultraviolet-visible (UV-Vis) region. The difference in bandgap values of Ag2S samples was located to be in the range of (1.5-2.05) eV. X-ray diffraction (XRD) measurements reflect the existence of polycrystalline, and the scanning electron microscopy images showed that the morphologies of Ag2S thin films have various microspheres. The summary of the samples in is in the annealing Ag2S thin film which exhibits the best photocatalytic application.
A review on contact lens inspection
Authors: Megat Abd Mana Nur Alifah , Chee Chin Lim , Yen Fook Chong , Yazid Haniza , Mohd Ali Yusnita
Published: June 2023

Over the year, contact lens detection has attracted attention and interest from many researchers to study further in this field of inspection. This paper provides a comprehensive review of the existing literature surrounding contact lens inspection methods. In this paper, contact lens-related, defects-related, and inspection methods related are described in detail. To detect contact lenses in a single image and also multi-image, numerous techniques have been developed and this paper is aimed at classifying and evaluating these algorithms. Also, contact lens inspection based on conventional and artificial intelligence methods will be discussed in detail. The industrial production process of contact lenses probably needs to be constructed with advanced tools based on recent technologies so that they can help in the inspection system to achieve accurate results of the inspection and reduce processing time. 
An improved features selection approach for control chart patterns recognition
Authors: Alwan Waseem , Akhmal Ngadiman Nor Hasrul , Hassan Adnan , Ramadhan Mohd Saufi Mohd Syahril , Ma’aram Azanizawati , Masood Ibrahim
Published: June 2023

Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using statistical process control (SPC). CCPs are widely used to improve production quality in many engineering applications. The principle is to recognize the state of a process, either a stable process or a deterioration to an unstable process. It is used to significantly narrow the set of possible assignable causes by shortening the diagnostic process to improve the quality. Machine learning techniques have been widely used in CCPs. Artificial neural networks with multilayer perceptron (ANN-MLP) are one of the standard tools used for this purpose. This paper proposes an improved features selection method to select the best features as input representation for control chart patterns recognition. The results demonstrate that the proposed approach can effectively recognize CCPs even for small patterns with a mean shift of less than 1.5 sigma. The dimensional reduction was achieved by employing Relief, correlation, and Fisher algorithms (RCF) for feature selection and (ANN-MLP) as a classifier (RCF-ANN). This study provides an experimental result that compares the performance before and after dimensional reduction.
Brain tumor detection in the Spark system
Authors: Benkrama Soumia , Elhouda Hemdani Nour
Published: June 2023

Machine learning (ML) and computer vision systems revolutionized the world, especially deep learning (DL) for convolutional neural networks, which has proven breakthroughs in brain tumor (BT) diagnosis. This study investigates a convolutional neural network (CNN) approach for image classification for BT detection using the EfficientNetB1 architecture with global average pooling (GAP) layers in a big data setting. A classification layer is done with a softMax layer. The system is created in the Apache Spark environment. Spark system is a unified and ultra-fast analysis engine for large-scale data processing. It is mainly dedicated to big data and deep learning (DL). Experiments are carried out using the brain magnetic resonance imaging (MRI) dataset containing 3,264 MRI scans to predict the performance of the model. The dataset is decomposed into two datasets. The model's performance was assessed and compared to existing models, it yielded a high precision, precision, and f1-score. In our work, we have achieved an accuracy of 97% and a performance of 98% on a dataset of 3,064 brain MRI images.
Comparative evaluation of data mining algorithms in breast cancer
Authors: Qahtan Alsudani Mustafa , Falah Fakhruldeen Hassan , Fayez Yousif Israa
Published: June 2023

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.
Unorthodox technique in sensing with the metamaterial-based resonator sensor at millimetre frequencies
Authors: Asghar Qureshi Suhail , Zainal Abidin Zuhairiah , A. Majid Huda , Y. I. Ashyap Adel , A. F. Esmail Bashar
Published: June 2023

A metamaterial-based resonator is presented in this paper for liquid-sensing applications. The designed sensor operates at millimetre-wave (mm-w) frequencies, and it can characterise the samples that may possess identical characteristics. This paper relies on the extracted permittivity of the structure in the characterisation of the samples, mainly liquids. The sensor requires a very small amount of samples for sensing and it is used in distinguishing oil, ethanol, methanol, glycerol and water. A shift in the resonance frequency of about 200 MHz per unit increase in the epsilon value of samples was achieved. The oil sample showed the lowest value in the extracted permittivity value, while water showed the nearest to zero extracted permittivity. This relationship of variation in extracted permittivity parameter with the change in the sample’s epsilon value is found to be linear and reliable regardless of the change in thickness of the sample.
Design of miniaturized multi-band hybrid-mode microstrip patch antenna for wireless communication
Authors: Azeez Yasameen , K. Abboud Maryam , R. Qasim Sara
Published: June 2023

This research presents a small, compact microstrip patch antenna of multi-band antenna. The multi-band antenna can be scaled to higher frequencies, making it possibly useful in 5G applications. The antenna is made up of asymmetric scalene triangles slots on the right side of the patch, which is provided by the reference patch antenna. The modes of antenna, patch and slots are excited to obtain four bands. The antenna design is 25×25×1.4 mm3 resonant at 3.93 GHz, 4.25 GHz, 5.37 GHz, and 6.18 GHz. The simulated design shows a peak gain of 4.44 dB at 3.93 GHz, 5.63 dB at 4.25 GHz, 5.91 dB at 5.37 GHz and 5.2 dB at 6.18 GHz. The total efficiency at 3.93 GHz and 4.25 GHz is -1.152 dB and -0.5174 dB, at 5.37 GHz and 6.18 GHz shows a total efficiency of -0.535 dB and -0.566 dB. Finally, the antenna is fabricated and measured. A good agreement shown between measurements and fabrication in terms of return loss.
For 5G applications, high-gain patch antenna in Ka-Band
Authors: Nafea Safa Nassr , Khamiss Nasser N.
Published: June 2023

The fifth generation (5G) of telecommunications is an attractive application for many researchers during the last years. The 5G requires high date rate with low latency communication medium and patch antenna was used recently for this application. In this article a patch antenna for 5G at 38 GHz was proposed. This article worked on enhancing the gain of patch antenna at Ka-Band and reducing side lobes level (SLL) in radiation pattern. The gain of the patch antenna was improved using a reflector layer located under antenna’s ground with free space air gap separating between two layers. Moreover, the SLL were reduced to satisfy low latency condition for 5G communications. The proposed antenna shows high gain around 8.10 dB with high front-to-back (F/B) ratio of 16.24 dB and wide operating bandwidth around 1.4 GHz for high data rate requirements as well as having reduced overall size antenna. The article based on analytical calculations for patch dimensions and optimization procedure to achieve the desired performance of antenna. Computer simulation technology (CST) had been used as an environment for simulation.
New approaches to the development of information security systems for unmanned vehicles
Authors: B. Ermukhambetova Bayana , A. Mun Grigoriy , B. Kabdushev Sherniyaz , B. Kadyrzhan Aruzhan , K. Kadyrzhan Kaisarali , S. Vitulyova Yelizaveta , E. Suleimenov Ibragim
Published: June 2023

A new approach to ensuring information security is proposed for using in the zone of direct radio visibility. The approach is based on a direct analogy between radio and optical radiation. In the latter case, information protection can be ensured by identifying the point or direction from which the signal comes; all other light sources are cut off by the observer. It is shown that a similar method of information protection can also be implemented in the radio range, which corresponds to modern trends in the development of radio holography. The simplest scheme for detecting the direction to a transmitter, identified as “friend” is presented. Its operability is demonstrated by means of simulation modeling. Ways for the further development of this approach are determined, based on the fact that modern technologies make it possible to implement distributed reception of a radio signal (to use a relatively large number of receivers), which makes it possible to implement direct analogues of such optical elements as a lens, holograms.
Selection of autofocus algorithms for printed circuit board automated optical inspection system
Authors: Putra Prastio Rizki , Wahyu Indrawan Rodik
Published: June 2023

This paper presents an examination study of 11 autofocus algorithms for printed circuit board (PCB) automated optical inspection (AOI). A selection of an optimal algorithm for that application based on some criteria was carried out. Unlike microscopy, PCB optical inspection does not require very high magnification. The object in this work was also different from that of microscopy and thus influenced the image features. We analyzed 47 PCB images, size of 640×480, sequentially captured every 1 mm in the z-direction. This work utilized USB digital microscope, and the magnification was set at ten times. Each algorithm calculated the sharpness values of the image sequences, and the plot of the sharpness profile was created. Moreover, the research also carried out experiments in several strategies, including image resizing and applying the non-local means (NLM) denoising filter to assess the algorithm performance in different situations. The algorithms were examined and ranked based on five criteria, i.e., computation time, full width at half maximum (FWHM), accuracy, number of half maxima, and range. The experimentation results showed that the Brenner gradient worked best for analyzing images both in their original dimension or resized images.
Dzongkha to English translation using the neural machine translation approach
Authors: Wangchuk Karma , Chinnaudayar Navaneethakrishnan Subalalitha , Jamtsho Yeshi , Wangchuk Yeshi
Published: June 2023

In this era of technology, a communication barrier is a thing of the past. With each passing day, different types of language-based applications are being launched. There are 109 official languages Google has translated to date. However, the Dzongkha translation has not been studied. The purpose of this paper was to study Dzongkha to English translation. The parallel corpus was collected from the Dzongkha development commission of Bhutan. The dataset consisted of 53018 sentence pairs. Unique words in Dzongkha and English were 13,393 and 12,506 respectively. Different neural machine translation models were implemented. The experimental results show that the bleu score of Seq2Seq models followed a fluctuating trend. However, the bleu score of the transformer model increases gradually. It was observed that the transformer outperformed the Seq2Seq models. The highest accuracy and the lowest training loss obtained were 84.46% and 0.014858 respectively with a bleu score of 64.89.
Footprint biometric authentication using SqueezeNet
Authors: Safie Sairul Izwan , Ramli Rusmawarni
Published: June 2023

Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.
A parallel algorithm to find the exact solution of the travelling salesman problem
Authors: Al-Neama Mohammed W. , Ahmed Iman Abdulwahab , Ali Salwa M.
Published: June 2023

The traveling salesman problem (TSP) is a problem in computer science that has been extensively studied and has a wide variety of real-world applications. It is considered an NP-hard issue since the only way to get a precise solution to it is to wait an exponentially long amount of time unless P=NP. Both accurate and heuristic algorithms exist to tackle this problem. The Branch-and-Bound algorithm (BnB) is often regarded as the most significant precise approach, despite the fact that it traverses, in the worst scenario, all potential tours in order to calculate the tour efficiently. This study proposed an efficient parallel algorithm to solve TSP by using a cluster multicore system. An exact algorithm will be used to get the optimal solution, and to expedite the search for the best path, we have employed a multi-threaded approach that capitalizes on the processing power of multiple CPU cores to concurrently process sub-problems.
An Otsu thresholding for images based on a nature-inspired optimization algorithm
Authors: Raheem Khamael Raqim , Shabat Hafedh Ali
Published: June 2023

Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.
Predictive analytics on COVID-19 data using Hive based on Hadoop cluster
Authors: Khaleel Ali Abbood , Kareem Ali Noori , Mahdi Laith Hikmet
Published: June 2023

COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data.