bluuebunny/crossref_metadata_2025 huggingface dataset:
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia
Authors: Hizqil Ahmad , Ruldeviyani Yova
Published: June 2024

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
Citrus leaf disease detection through deep learning approach
Authors: Islam Sk. Fahmida , Chakrabarty Nayan , Uddin Mohammad Shorif
Published: June 2024

The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.
Probabilistic load flow based voltage stability assessment of solar power integration into power grids
Authors: Rehiara Adelhard Beni , Bawan Elias Kondorura , Palintin Antonius Duma , Wihyawari Bibiana Rosalina , Setiawan Paisey Fourys Yudo , Pasalli Yulianus Rombe
Published: June 2024

The interconnection of renewable energies increases the complexity of modern power systems. Hence, stability assessments should be made to ensure the system’s stability after penetration. Solar power is a type of renewable energy that has become a widespread energy source among renewable energy sources. About 1 MWp of the solar power plant has been prepared to be interconnected to the IEEE 8 bus of Manokwari grid, and this paper investigates the voltage profile, power losses, and stability of the solar power plant penetration using an adaptive kernel density estimator (AKDE) and compares it to a Monte Carlo simulation (MCS)-based probabilistic load flow (PLF). About 5000 samples have been used to test the grid after the connection. Results of simulations show that the solar penetration can reduce power losses from 0.4084 MW to 0.3080 MW and 0.3045 MW by the proposed method and MCS method, respectively, and further increase the bus voltage profile. The power network has the stability to be connected to solar power, as indicated by the small stability index values of each bus. The proposed method using the AKDE method has a more accurate result in stability indices indicated by small fast voltage stability index (FVSI), line stability index (Lmn), and line stability factor (LQP) indices.
A novel framework of building operation algorithm for the block of technical diagnostics of aircraft’s automatic control system
Authors: Vuong Trung A. , Tran Dong LT. , Vo Thanh C. , Nguyen Minh T. , Tran Hoang T.
Published: June 2024

This article presents the problem of designing an automatic control system that is stable against errors and failures of sensors on aircraft. The sensor system has a technical diagnostic block that ensures diagnosis and eliminates typical errors and failures. Based on the determination of the error vector, damage can occur by adding measurement elements corresponding to the measurement parameters to the control system. When there are errors or failures of the sensor elements, the state vector of the system changes and is determined by measurements. The difference between the measured vector components when there are errors, failures and when working normally is the basis of the working algorithm of the failure diagnosis block. The results demonstrate encouraging prospects for practical implementations.
Internet of things-based rice field irrigation evaporation monitoring system
Authors: Aisyah Putri Yeni , Widya Pratama I Putu Eka , Rahmadhana Furqan , Al Ghifari Muhammad Ghozi
Published: June 2024

The urgency for efficient irrigation in Indonesia’s agriculture sector, particularly in paddy fields, is evident. However, existing methods for monitoring water levels are antiquated, often requiring manual measurements with a ruler. This research introduces a comprehensive “monitoring system for light intensity and water temperature as an analysis of evaporation for rice irrigation based on the internet of things”. The system integrates various sensors an anemometer for wind speed, an ultrasonic sensor for water level, a DS18B20 waterproof sensor for water temperature, and a GY-8511 sensor for sunlight intensity. All data are collected by an Arduino Mega controller, connected to an ESP32 for transmitting the readings to the Blynk app and an I2C 20×4 liquid crystal display (LCD) screen. The control mechanism employs a closed-loop system with a direct current (DC) motor actuator to operate the water gate, which can also be manually controlled via a cellphone. The system effectively meets daily evapotranspiration requirements of 1.44 mm, with optimal conditions yielding water levels of 3 cm, water temperatures of 38.53 °C, sunlight intensity of 4.59 mW/cm², and wind speed of 0.21 m/s.
A review on microstrip patch antenna for wireless communication systems at 3.5 GHz
Authors: Rana Md. Sohel , Chakrobortty Paris , Pal Sourav , Rahman Md. Mahmudur , Sarker Arpon , Saha Pranto , Rahman Sohanur
Published: June 2024

This article presents a review of several microstrip patch antennas for 3.5 GHz wireless applications. Different substrate materials, FR-4 (loss), FR-4 Epoxy, Rogers RT/droid 5880, TLC-30, and Rogers RT/droid 5880 LZ, are used. In recent years, wireless antenna applications have increased, including biomedical appliances, internet of things (IoT) terminals, edge devices, radars, mobile phones, and many more. In this work, several articles were reviewed and investigated, and several microstrip patch antennas with a resonance frequency of 3.5 GHz were designed using different substrate materials and shapes. This article also discussed the geometric shapes of antennas, antenna properties, sizes of substrate materials, loss tangent, thickness, return loss, bandwidth, voltage standing wave ratio (VSWR), gain, efficiency, and directivity. Several software is used for design and simulation, including computer simulation technology (CST), high-frequency simulation frequency (HFSS), and advanced design system (ADS), FEKO, and MATLAB. The main goal of this paper is to talk about different wireless application papers that work in the S-band at a frequency of 3.5 GHz and have been published in various international journals and conferences.
Energy saving performance analysis for future fifth generation millimetre-wave cellular networks
Authors: Anwar Apandi Nur Ilyana , Muhammad Nor Aishah
Published: June 2024

The deployment of fifth generation (5G) millimetre-wave (mmWave) base stations (BSs) will consume more energy over time due to the limited time available, despite the increasing interest in developing 5G mmWave wireless communication technology. Constructing 5G mmWave cellular network infrastructure can improve energy efficiency, which is a challenge to implement in heterogeneous networks. This paper presents analytical frameworks for monitoring the effectiveness of 5G mmWave cellular networks. Based on the state management of BS, a system model for 2-tier heterogeneous networks is developed, and particle swarm optimization (PSO) is then used to compute the total energy consumption of the heterogeneous networks. Energy consumption was compared and analysed by leveraging state switching and the aggregate delay for three methods: fundamental separation, conventional separation, and a proposed energy-saving method that introduced a sleep state. Simulation shows that the proposed energy-saving method, which is a combination of conventional separation approaches, has the lowest total energy consumption and offers a 9% reduction compared to other related works. The results validate the accuracy of the power usage used in the 5G mmWave cellular network of the proposed method.
Stability analysis of power system under n-1 contingency condition
Authors: Baleboina Guru Mohan , Rudramoorthy Mageshvaran
Published: June 2024

Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.
Data access control for named data of health things
Authors: EL-Bakkouchi Asmaa , EL Ghazi Mohammed , Bouayad Anas , Fattah Mohammed , EL Bekkali Moulhime
Published: June 2024

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.
Optimized k-nearest neighbours classifier based prediction of epileptic seizures
Authors: Jagath Prasad Himayavardhini , Marjorie S. Roji
Published: June 2024

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.
Potentiality of graphene as a base material for impact ionization avalanche transit time diode in high-frequency applications
Authors: Swain Mamata Rani , Tripathy Pravash Ranjan
Published: June 2024

In this paper, the microwave application potential of graphene is studied using a double-drift-region (DDR) impact ionization avalanche transit time (IMPATT) diode. The simulation of this diode is carried out for the very first time at several different atmospheric window frequencies. Because graphene has unique and special properties, it could be used to make electronic gadgets for the next generation. The device is simulated at a variety of millimeter and sub-millimeter wave frequencies using a model called self-consistent drift diffusion (SCDD), which was developed by the author based on current continuity, Poisson’s equation and space charge equation. When compared to traditional IMPATT devices such as Si, GaAs, InP and GaP, the results demonstrate superior performance in terms of efficiency, and RF power across a wide range of operating conditions. Again, the behavior of noise in graphene IMPATT is studied, and it is found that it makes less noise than Si and GaAs IMPATT. The simulation results open up new avenues for IMPATT diode manufacture and design.
Fifth generation core: the performance enhancement of virtual private server and bare metal
Authors: Putri Hasanah , Hikmaturokhman Alfin , Ahmad Izanoordina , Anwar Radial , Akbar Rafli
Published: June 2024

The fifth generation (5G) architecture represents the most recent advancement in mobile networks and is presently operational in various global places. Several new use cases and applications have been introduced, with a specific focus on improving throughput, reducing latency, minimising packet loss, optimising CPU usage, and maximising memory utilisation. In order to effectively address each scenario, it is necessary to integrate the most advanced technology, putting in significant effort to optimise resources and ensure system adaptability. This strategy will establish an architecture capable of accommodating many scenarios of a shared physical infrastructure by using techniques such as virtualization and cloud-based service deployment. Therefore, in this study, a test was carried out related to the performance of the 5G core network (CN) on bare metal servers and virtual private servers (VPSs). The quality of service (QoS) using Wireshark and Iperf3 is tested by utilizing ‘cpustat’ and free tools. The results of performance comparisons of these two methods on the 5G CN shows throughput values of ≥10 Gbps ≤20 Gbps, latency values of ≤4 ms, and packet loss values of 0%, in accordance with IMT 2020 standards. Thus, the ideal 5G CN services can be realized.
Systematic literature review on evaluation models and methods in enterprise architecture research
Authors: Khairina Dyna Marisa , Purwanto Purwanto , Kusumo Nugraheni Dinar Mutiara
Published: June 2024

Several enterprises implemented enterprise architecture (EA) projects to align business and information technology (IT) strategies. The evaluation process is needed to ensure the implementation of EA projects provides effectiveness, efficiency, and feasibility of EA information systems (IS) and assesses previous project experience to avoid future EA project risks. The study aims to present a systematic literature review (SLR) of the models and evaluation methods used or developed, especially in the field of EA research. Based on the inclusion and exclusion criteria, 21 articles were selected for review. The results of the study present an overview of the models and methods used as well as new approaches developed for EA evaluation as well as information based on approaches related to models and methods identified as organizing information and data analysis to broaden future research insights. The literature review also provides additional simple theories related to the implications and techniques of the identified models and methods. The study contributes to company stakeholders to encourage the implementation of EA, identify improvements and enhancements to EA projects as well as further references and insights for practitioners and researchers regarding EA evaluation as an effort to assess the success of achieving enterprise goals.
Authentication with privacy-preserving scheme for 5G-enabled vehicular networks
Authors: Hamdan Mays A. , Makhlouf Amel Meddeb , Mnif Hassene
Published: June 2024

The 5G-enabled vehicular network is an innovative technology that has promise for intelligent transportation systems. It enables the transmitting of messages about traffic that deliver the most recent information on congestion, road conditions, and driving surroundings. The communication channel used by vehicle networks is inherently open, which unfortunately exposes the system to privacy and security concerns. To solve the problems of deploying a safe vehicular network, some academics have put forth plans. However, a number of current methods have significant computational or communication overhead costs. To solve this problem, an efficient and secure authentication with a privacypreserving (ES-APP) scheme established elliptic curve encryption is introduced. With the proposed ES-APP, the data signed and verified for vehicle-to-vehicle and vehicle-to-infrastructure modes in the 5G-based vehicular network are more effective. The ES-APP scheme’s goal is to meet the criteria for the security and privacy of the 5G-enabled automotive network. Ultimately, this work discusses the critical survey of the existing studies and the expected outcome for the ESAPP scheme and further works in the 5G-enabled vehicular network.
A clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network
Authors: Chaibi Loubna , Sebgui Marouane , Bah Slimane
Published: June 2024

The current paper presents a clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network (FANET). The technological advances of the last decade gave rise to emerging technologies. Unmanned aerial vehicles (UAVs) are small aircraft that proved their usefulness for different tasks nowadays. They can collaborate to achieve missions especially in areas where traditional networks cannot work or cannot accede. A FANET is composed by a number of these aircraft. For alike networks, the resources are limited. Indeed, an efficient energy management is required to extend the life of the network. This work is a clustering method for heterogeneous nodes of a FANET, each node is equipped with one sensor, and four different sensors are used. Clustering is grouping nodes with the aim of efficiency improvement. The clustering is done before the beginning of the rescue mission and depends on the types of sensors the nodes are equipped with and. The master election depends on the available energy of each one of the nodes. The simulation is done with a discrete event simulator (DES) and the results are compared to the algorithm of glowworm swarm optimization (GSO) to demonstrate the effectiveness of the suggested technique.
Pre-slippage detection and counter-slippage for e-pattern omniwheeled cellular conveyor
Authors: Keek Joe Siang , Loh Ser Lee , Hanafi Ainain Nur , Cheong Tau Han
Published: June 2024

This paper presents continuation work of e-pattern omniwheeled cellular conveyor (EOCC) since its first introduction. EOCC is a conveyor that is modular and is made up of omniwheels arranged horizontally and vertically. Although in the last published paper, the EOCC had been proven to be capable of transporting box omnidirectionally and achieving yaw control concurrently, however, due to the natural properties of omniwheel, the performance is jeopardized by slippage. While minor slippage can be negligible, but a major slippage can eventually destroy the whole trajectory tracking performance. Therefore, counter-slippage methods are proposed in this paper. The simulation results show that the proposed counter-slippage method significantly improves the trajectory tracking performance up to 42% of reduction in integral of absolute error. Moreover, in this paper, pre-slippage detection method, which aims to perform early detection of slippage, is being presented as well. Although these proposed methods are simple, but they are proven to have achieved improved tracking performance than conventional controller, as presented in this paper.
Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network
Authors: Abdullah Nor Paezah , Kassim Murizah , Mohd Deni Sayang , Mohd Yussoff Yusnani
Published: June 2024

QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.
Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell
Authors: Pangaribowo Triyanto , Mulyo Utomo Wahyu , Budiman Abdul Hamid , Abu Bakar Afarulrazi , Khaerudini Deni Shidqi
Published: June 2024

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.
An interior penalty function method for solving fuzzy nonlinear programming problems
Authors: Govindhasamy Vanaja , Kandasamy Ganesan
Published: June 2024

In this article, we investigate fuzzy interior penalty function method for solving fuzzy nonlinear programming problems (FNLPP) based on a new fuzzy arith-metic, unconstrained optimization, and fuzzy ranking on the parametric form of triangular fuzzy numbers (TFN). The main objective of this paper is to solve constrained fuzzy nonlinear programming problems using interior penalty func-tions (IPF) by converting it into unconstrained optimization problems. We prove an important lemma and a convergence theorem for the interior penalty functions method. Interior penalty function techniques favor sites near the boundary of the feasible region in the interior. We present a numerical example of the suggested method and compare the results to those produced by existing methods.
Comparative harmonic elimination techniques for supraharmonic reduction in microgrid
Authors: Siva Ayyar Subramaniya , Ramesh Kumar Sakunthala Ganesan , Dhayalini Karuppiah
Published: June 2024

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.
An efficient intrusion detection systems in fog computing using forward selection and BiLSTM
Authors: Abu Zwayed Fadi , Anbar Mohammed , Manickam Selvakumar , Sanjalawe Yousef , Alrababah Hamza , Hasbullah Iznan H. , Almi’ani Noor
Published: June 2024

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.
A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms
Authors: Hasan Nahid , Polin Johora Akter , Ahmmed Md. Rayhan , Sakib Md. Mamun , Jahin Md. Farhan , Rahman Md. Mahfuzur
Published: June 2024

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
Skin cancer classification using EfficientNet architecture
Authors: Harahap Mawaddah , Husein Amir Mahmud , Kwok Shane Christian , Wizley Vincent , Leonardi Jocelyn , Ong Derrick Kenji , Ginting Deskianta , Silitonga Benny Art
Published: June 2024

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.
Exploring the elderly user experience in using digital health interventions: a case study of the MySejahtera application
Authors: Ahmad Nahdatul Akma , Rosdi Rosidah , Alias Nursyahidah , Zainal Azaliza , Abdul Aziz Nur Farhanum
Published: June 2024

In recent years, there has been increased interest in using digital health tools to improve healthcare outcomes and encourage healthy lifestyles. However, older adults, who often face challenges with technology, may encounter difficulties when using these tools. This study focused on understanding how elderly individuals experience the MySejahtera app, a digital health tool designed to help manage health during a pandemic. The research involved 30 elderly users through focus groups, interviews, and user experience evaluations. The study identified key themes in the elderly user experience, such as the need for simplicity, efficiency in the app's interface, ease of learning, concerns about security, and emotional reactions. Recommendations were made to enhance the engagement of elderly users with digital health apps. While the MySejahtera app shows promise for older adults, the study highlights the importance of addressing specific design considerations and providing support to improve user satisfaction. Overall, the research offers valuable insights and recommendations for designing and implementing digital health applications that better meet the needs and preferences of elderly users.
Effect of high resistivity soil under high impulse currents
Authors: Syed Abdullah Syarifah , Idris Nur Farahi , Mohamad Nor Normiza , Ahmad Nurul Nadia , Mahmud Azwan
Published: June 2024

In this paper, experimental test results of several ground electrodes surrounded with gravelly soil medium subjected to high impulse currents were studied, to investigate the effect of confined soil surround electrodes. Ground resistance measurements were performed at low magnitude of voltage and current, where the results are compared to the impulse characteristics of ground electrodes. This paper shows a significant difference in the RDC values and impulse characteristics of ground electrodes when gravelly soil medium surrounded the ground electrode in comparison to the electrodes installed in natural soil. This indicates that the confined soil around the electrode has a major effect on the performance of ground electrodes, whether at steady state or under high impulse conditions. Equivalent circuit for each tested electrode was developed with personal simulation program with integrated circuit emphasis (PSPICE), where the effect of inductance was seen in the electrodes surrounded with gravelly soil.
Applications of nanostructured materials for severe acute respiratory syndrome-CoV-2 diagnostic
Authors: Turab Nidal M. , Abu Owida Hamza , Al-Nabulsi Jamal I. , Alnaimat Feras
Published: June 2024

There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS‑CoV‑2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.
Optimization of a CH3NH3SnI3 based lead-free organic perovskite solar cell using SCAPS-1D simulator
Authors: Rana Md. Sohel , Abdur Razzak Md.
Published: June 2024

In this study, a CH3NH3SnI3-based perovskite PV cell with the structure (FTO/TiO2/CH3NH3SnI3/Cu2O) was made and optimized by changing the layer thickness, defect density, and doping profile using the solar cell capacitance simulator (SCAPS) 1D simulator. To better understand how the device interface affects carrier dynamics, a synergic optimization of the device is done by altering the electron-transport layer (ETL) and hole-transport layer (HTL) materials. The light glows through the window layer of Sn2O: F, which serves as the transparent conducting oxide layer in our suggested cell construction and then travels over TiO2 as an n-type ETL. Due to its unique features, the p-type perovskite (CH3NH3SnI3) is chosen as the primary absorber layer. Lastly, Cu2O is added as an HTL before the back contact because it has a higher hole conductivity and the proper offsets for spreading the valance and conduction bands. Additionally, Cu2O-based devices outperform frequently utilized spiro-OMeTAD-based devices in terms of efficiency. According to the findings of these simulations, the optimized structure has a power conversion efficiency (PCE) of 41%, an open-circuit voltage of 1.32 V, a short-circuit current density of 34.31 mA/cm2 and a fill factor (FF) of 90.5%. Additionally, the optimized structure has a short-circuit current density of 34.31 mA/cm2.
Effective capacity analysis of configurable intelligent surface-assisted NOMA communications systems
Authors: Q. Tran Huu , Van Khuong Ho
Published: June 2024

This paper investigates the integration of configurable intelligent surfaces (CIS) into relay radio networks, focusing on communication system enhancement. Towards this end, we propose CIS-assisted non-orthogonal multiple access (NOMA) communication systems to improve direct connections between a base station and two destination nodes. Our primary objective is to assess the net-work’s overall capacity, considering critical factors like signal-to-noise ratio, the number and placement of CIS components, quality of service exponent, and power distribution coefficients. Analytical equations developed in this research closely align with simulation results, validating our theoretical analysis. This study underscores the growing significance of CISs in modern communication systems, introducing adaptability and optimization to wireless networks. By exploring CIS-assisted NOMA communication systems, we contribute to dis-cussions about the evolving landscape of wireless communication technologies, poised to revolutionize information transmission and reception in the digital age.
Virtual teaching and learning for autistic students amidst the pandemic: a systematic literature review
Authors: Ab Mahadi Mudrikah , Yahya Norziana , Akma Ahmad Nahdatul , Ahmad Ruzita , Mohd Yusof Ernie Mazuin
Published: June 2024

Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educators. This systematic literature review (SLR) aimed to explore the effectiveness of virtual teaching and learning (VTL) by employing the reporting standards for systematic evidence syntheses (ROSES) framework. Articles from databases like Scopus, Web of Science, and Google Scholar were systematically examined, focusing on themes such as support, coping strategies, teaching methods, flexibility, and communication. The review identified 14 sub-themes within these categories, providing tailored coping and teaching strategies for parents, teachers, and caregivers working with autistic students. From 706 initially identified articles, 376 were selected, with 17 specifically relevant to virtual teaching for autistic students during the pandemic. These findings contribute insights to the existing literature and offer practical implications to enhance VTL experiences for autistic students facing pandemic challenges.
Swin transformer adaptation into YOLOv7 for road damage detection
Authors: Irsal Riyandi Banovbi Putera , Utaminingrum Fitri , Ogata Kohichi
Published: June 2024

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.
Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review
Authors: Petkov Nikolay , Ivanova Malinka
Published: June 2024

Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.
Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones
Authors: Yandouzi Mimoun , Berrahal Mohammed , Grari Mounir , Boukabous Mohammed , Moussaoui Omar , Azizi Mostafa , Ghoumid Kamal , Kerkour Elmiad Aissa
Published: June 2024

Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
Towards visual sentiment summary to understand customers’ satisfaction
Authors: Thanh Loan Dao Thi , Tuan Tran Anh
Published: June 2024

Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.
Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit
Authors: Basavarajaiah Shivarudraiah , Garudachar Raju
Published: June 2024

An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.