bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluate of vest massage therapy with rotating pressure based on pre-experimental methods
Authors: Loniza Erika , Aqmariah Mohd Kanafiah Siti Nurul , Jusman Yessi , Zakaria Zulkarnay
Published: January 2025

Many postpartum mothers complain that their milk production is too low to supply the baby’s needs. There are two essential substances in the milk: the prolactin hormone and the oxytocin hormone. Consequently, there are two ways to stimulate these hormones: massage techniques such as breast care and oxytocin massage. This study aims to design vest therapy devices to expedite breast milk production. With the use of vest therapeutic devices, it can be observed that the amount of breast milk production increases. This research uses a pre-experimental method in postpartum mothers, which uses the vest massage therapy and does not use the vest massage therapy. Accidental sampling was used as the sampling method for this study, and the data were analyzed using the independent t-test. It is hoped that making Vest therapy devices can facilitate breastfeeding for postpartum mothers with the aim that they can increase the amount of breast milk and supply the milk for the babies in the early stage of their life. The test result discovered an increase in breast milk volume in breastfeeding mothers by an average of 7.3 ml in postpartum mothers who used vest therapy equipment compared to the previous amount of milk produced.
Overcurrent effects on copper insulated PVC cables and fire resistance via thermal imaging and macrostructure analysis
Authors: Ali Akbar Muhammad , Humaidi Syahrul , Tarigan Kerista , Ramdan Dadan , Frida Erna , Siregar Yulianta
Published: January 2025

This study investigates the effects of overcurrent on copper (Cu) insulated polyvinyl chloride (PVC) cables, focusing on their thermal behavior and fire resistance. We utilized thermal imaging, macrostructural analysis, and Joule heating calculations to evaluate six cable samples subjected to various currents. Results showed that with increasing current, the temperature of the cables rose significantly. For example, the CC0 sample, with no current, had a temperature of 36 °C, while the CC110 sample, subjected to 110 A, reached 1,091 °C. Joule heating calculations indicated energy values ranging from 0 J for the CC0 sample to 7,260,000 J for the CC110 sample. Physical observations included minor deformations at 253 °C and complete insulation loss at 1,091 °C. These findings emphasize the critical need for managing overcurrent to prevent severe cable damage and enhance system safety. This research provides practical insights for optimizing cable design and improving thermal management, offering valuable contributions to electrical engineering practices.
Conceptual design for traceability and transparency in halal self-declared with blockchain
Authors: Munawar Munawar , Zulfiandri Zulfiandri , Mugiyono Arif
Published: January 2025

Halal self-declared is a halal certification procedure for small micro enterprises (SMEs). However, sufficient technology support is required to ensure the halal process’s transparency and traceability. By integrating blockchain-oriented software engineering (BOSE) technology with smart contracts and electronic product code information services (EPCIS), this study aims to deliver a conceptual design for traceability and transparency in halal self-declared. The effectiveness of the blockchain in storing data and disclosing private information to interested parties can be circumvented by utilizing both off-chain and on-chain technology. The effectiveness of blockchain data storage and the ability to disclose sensitive information to interested parties can be advantageous for both on-chain and off-chain applications.
Recent developments in vehicle routing problem under time uncertainty: a comprehensive review
Authors: Yernar Akhmetbek , Turan Cemil
Published: January 2025

This review paper examines recent advancements in vehicle routing optimization under time uncertainty, focusing on the vehicle routing problem (VRP). It sys-tematically analyzes research papers to identify strategies for optimizing routes despite temporal uncertainties, covering key areas such as optimization algo-rithms, uncertainty modeling techniques, and simulation methods. The study investigates dynamic dispatching models, reliability considerations, and multi-objective optimization approaches. By synthesizing existing literature, this pa-per presents the current state of research in vehicle routing under time uncer-tainty and suggests potential future research directions. Our findings indicate that integrating robust optimization techniques with advanced simulation meth-ods could significantly enhance decision-making processes in uncertain envi-ronments. Additionally, the paper highlights the role of machine learning and artificial intelligence in developing adaptive algorithms that respond to dynamic changes in real-time. As the need for efficient logistics solutions grows, this comprehensive review underscores the importance of addressing uncertainties in vehicle routing to improve operational efficiency, reduce costs, and enhance customer satisfaction.
Advancing palm oil fruit ripeness classification using transfer learning in deep neural networks
Authors: Kurniawan Rudi , Samsuryadi Samsuryadi , Susilawati Mohamad Fatma , Oktafia Lingga Wijaya Harma , Santoso Budi
Published: January 2025

The palm oil industry is a significant component of Indonesia’s economy, driven by increasing global demand across various industries. Manual identification of palm oil fruit ripeness is often subjective and labor-intensive, creating a need for a faster and more accurate solution. This study proposes the use of deep learning models based on transfer learning to enhance the classification of palm oil fruit ripeness. Our research evaluates several models, finding that ResNet152V2 achieves the highest performance with superior accuracy and the lowest validation loss. DenseNet201, MobileNet, and InceptionV3 also deliver strong results, each demonstrating an accuracy above 0.99 and a validation loss below 0.04. Cross-validation confirms that ResNet152V2, DenseNet201, and MobileNet maintain high and consistent performance across different folds, showcasing their stability and reliability. This approach provides a promising alternative to manual methods, offering a more efficient and precise means for determining palm oil fruit ripeness, which could significantly benefit the industry by streamlining quality control processes.
A stochastic optimization called split-compete optimization and its utilization on economic load dispatch problem
Authors: Daru Kusuma Purba , Candrasyah Hasibuan Faisal
Published: January 2025

The economic load dispatch (ELD) problem is one crucial optimization problem in a power system. Metaheuristics has become a standard method to tackle this problem. Ironically, ELD is not popular enough to become a constrained use case in most studies introducing new metaheuristics, where four designs in mechanical engineering have become the most popular ones. This work introduced a new metaheuristic called split-compete optimization (SCO). It contains three serial steps where two directed searches are employed and competed against each other in each step. SCO is then assessed to tackle both unconstrained and constrained problems where 23 classic functions represent the unconstrained problems and two cases in ELD problems represent the constrained ones. Five new metaheuristics are chosen as competitors, including the coati optimization algorithm (COA), language education optimization (LEO), northern goshawk optimization (NGO), Kookaburra optimization algorithm (KOA), and walrus optimization algorithm (WaOA). In the first evaluation, SCO is superior enough by being better than COA, LEO, NGO, KOA, and WaOA in 14, 13, 20, 15, and 10 functions, whereas SCO is dominant in high-dimensional functions. Meanwhile, SCO is competitive in solving both cases of ELD problems.
An efficient clustering approach in electrical energy consumption patterns
Authors: Tiara Kusuma Dine , Ahmad Norashikin , Sakinah Syed Ahmad Sharifah , BM Sangadji Iriansyah , Arvio Yozika
Published: January 2025

A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF). The objective of this research is to determine the efficacy and benefits of each clustering technique employed in load profile analysis. The clustering algorithm approach used in this research is k-means and fuzzy subtractive clustering (FSC). The trials carried out on these two approaches provide valuable knowledge regarding the effectiveness and superiority of each algorithm in producing significant clusters from the data used in this research. The evaluation conducted using the Davies-Bouldin index (DBI) indicates that the quality value for FSC is 0.25 for business customers and 0.31 for industrial customers. On the other hand, the quality value for k-means is 0.55 for business customers and 0.56 for industrial customers.
Optimizing the best student selection: hybrid K-Means approach and entropy-grey relational analysis
Authors: Sulistiani Heni , Setiawansyah Setiawansyah , Palupiningsih Pritasari , Ferico Octaviansyah Pasaribu Ahmad , Andika Rio , Hamdan Sobirin Muhammad
Published: January 2025

The selection of the best students is an important process in recognizing students' achievements and dedication in various fields. Through careful and fair selection, students who stand out in both academic and non-academic terms can be identified and assigned. The purpose of the research on the use of hybrid entropy-grey relational analysis (GRA) and K-Means clustering in the selection of the best students is to develop a more objective, accurate, and comprehensive assessment system. The silhouette score results show that 2 clusters have a value of 0.5733, so in this study 2 clusters are used with the best cluster at cluster 0. Data from cluster 0 will be used in determining the best students using hybrid entropy-GRA. The results of the best student ranking using the hybrid entropy-GRA method, for the first best student with a final score of 0.25 were obtained by Mareta Amelia. The hybrid approach of K-Means and entropy-GRA offers a powerful tool to improve decision-making in the student selection process. The hybrid approach of K-Means grouping and entropy-GRA presents a powerful solution, improving the decision-making process and ensuring that high-achieving students are accurately recognized and rewarded.
Graphene based nano-antenna for wireless communication systems at terahertz band
Authors: Singh Raj Kumar , Mamta Kumari , Kumar Dhirendra
Published: January 2025

The need for nano-antennas with decreased size and the capacity to operate at mid-infrared frequencies to enable adequate coverage of signal is being investigated. In this paper, we present graphene-based nano-antenna and analysed at the resonating frequency 33 THz using gallium arsenide material as a substrate having dielectric constant 11.35 and a loss tangent of 5.6×10-4 for terahertz (THz) frequency. The height of substrate is optimized to 108 nm and in-plane dimension being 1,700×1,400 nm. Graphene was used as a rectangular patch with dimension 850×450×5 nm and ground having chemical potential=1.4 eV, and relaxation time=1 ps, to achieve high gain and bandwidth. Impact of slot width variation on the antenna parameters have been reported in terms of reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern and gain. Reported beam width being 90.4° for both electric and magnetic planes. Proposed antenna achieved a return loss of -18.38 dB, VSWR less than 2, indicating good match with load, highest gain of 8.8 dBi and bandwidth of 500 GHz at the target resonance frequency making it suitable for 5G/6G mm wave wireless communication.
Glioma segmentation using hybrid filter and modified African vulture optimization
Authors: Kuntiyellannagari Bhagyalaxmi , Dwarakanath Bhoopalan
Published: January 2025

Accurate brain tumor segmentation is essential for managing gliomas, which arise from brain and spinal cord support cells. Traditional image processing and machine learning methods have improved tumor segmentation but are often limited by accuracy and noise handling. Recent advances in deep learning, particularly using U-Net and its variants, have achieved significant progress but still face challenges with heterogeneous data and real-time processing. This study introduces a hybrid bilateral mean filter for noise reduction coupled with an ensemble deep learning model that integrates U-Net, InceptionV2, InceptionResNetV2, and W-Net to enhance segmentation accuracy and efficiency. Additionally, we propose a novel modified African vulture optimization algorithm (MAVOA) to further refine segmentation performance. Evaluated on the BraTS 2020 dataset, our model achieved a loss of 0.023 with strong performance metrics: 98.2% accuracy, 97.2% mean intersection over union (IOU), and 99.1% precision. It effectively segmented glioma subregions with dice scores of 0.96 for necrotic areas, 0.97 for edema, and 0.91 for enhancing regions. On the BraTS 2021 dataset, the model maintained high accuracy 96.4%, mean IOU 95.9%, and dice coefficients of 0.91 for necrotic areas, 0.95 for edema, and 0.92 for enhancing regions.
Arrhythmia classification using CMF-AFF based on electrocardiogram in field programmable gate array device
Authors: Revanth Nalavade , Anto Bennet Maria
Published: January 2025

Arrhythmia classification is categorization of irregular heart rhythms depending on patterns detected in electrocardiogram (ECG) signals assist in treatment and diagnosis of cardiac conditions. ECG evaluates heart’s electrical activity to diagnose various heart conditions, but it is affected by interference or noise. ECG’s signal filtering is essential pre-processing stage that minimizes noise and highlights wave characteristics in ECG data. However, digital filters are normally constructed by multiplying coefficient and then multiplying value given as feedback which leads to more power and area consumption. To solve these issues, coefficient memory compression (CMC) technique is proposed with an adaptive FIR filter (AFF) to achieve low area and low power dissipation by compressing memory requirements in a field programmable gate array (FPGA). An adaptive FIR filter is employed to effectively minimize noise like baseline noise, muscle contraction noise, and low-frequency noise. The performance of CMC-AFF is analyzed in terms of look up table (LUT), register, digital signal processing (DSP), power, and global buffer (BufG). The proposed approach achieves a low power consumption of 0.012 W in Zed Board Zynq7000 AP system on chip (SoC) FPGA device compared to existing techniques like collateral and sequence approaches using Bartlet filter and low-power ECG processor using Bartlet filter respectively.
Internet of things-drone trajectory planning model with edge computing based on long range payload in rural areas
Authors: Prasetyo Nugroho Eddy , Djatna Taufik , Sukaesih Sitanggang Imas , Hermadi Irman
Published: January 2025

The integration of internet of things (IoT) with unmanned aerial vehicle (UAV) or drone, for precision agriculture (PA) in rural tea plantations is required to ensure optimal outcomes. However, rural settings presents exceptional challenges for data transmission, particularly in maintaining effective communication between drone and ground control stations (GCS). Therefore, this research aimed to develop a payload metadata identification model using long range (LoRa) technology, known for robust IoT capabilities of the model. LoRa was used to transmit drone data packets to GCS, including image data computations and onboard sensor information. Additionally, the research proposed IoT-drone trajectory planning model, specifically designed for PA in rural tea plantations. This model incorporated LoRa technology for data transmission, leveraging the effectiveness of the model in remote areas. Edge computing was also integrated into model to classify the suitability of tea plantation picking areas based on image captured with drone. An important component of the research was trajectory planning system, which optimized drone flight paths by considering location data, throughput data, battery energy consumption, and the computation of suitable picking locations. Finally, experimental results showed the effectiveness of the proposed model in identifying payload metadata, monitoring drone trajectory, and optimizing picking location paths in rural tea plantations.
Optimizing plant health monitoring: improved accuracy and the computational efficiency with stacked machine learning models and feature filtering
Authors: Sangeetha Tupili , Ezhumalai Periyathambi
Published: January 2025

Plant cultivation can be effectively achieved with the help of hydroponic farming that allows growing soilless and organic plant veggies. However, maintaining optimal plant health in such controlled environments requires continuous monitoring and assessment techniques. This paper provides a comprehensive description of how to determine and categorize the health of hydroponic plants based on a wide range of parameters, such as temperature, pH, electrical conductivity (EC), leaf count, plant height, and vegetative indices. We present a novel approach termed “Hybrid XGBoosting” that combines the multi-classification algorithm extreme gradient boosting (XGBoost) with gradient-based one-side sampling (GOSS) methods to improve accuracy and processing efficiency. This approach first adopts a feature correlation method known as “Pearson’s correlation” for reducing repeated data that are directly proportional or inversely proportional to each other. Finally, we perform a thorough comparative study using well-known algorithms including traditional XGBoost, AdaBoost, and gradient boosting. We demonstrate the better prediction capabilities of Hybrid XGBoosting with 97.93% accuracy through rigorous testing and evaluation, showing its potential for improving hydroponic plant health assessment approaches. Additionally, our research employs comprehensive algorithm assessment measures, such as root mean squared scaled error (RMSSEE), to guarantee the stability and reliability of the results.
Predicting demand in changing environments: a review on the use of reinforcement learning in forecasting models
Authors: Rolando Neira Villar José , Angel Cano Lengua Miguel
Published: January 2025

This systematic review, carried out under the PRISMA methodology, aims to identify how reinforcement learning has been used in demand forecasting, distinguishing the problems they are trying to overcome, recognizing the algorithms used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying the business sectors in which it has been developed. Studies from all sectors were considered to expand the search range. A total of 24 articles were qualitatively analyzed, and the main results were that reinforcement learning has been used mainly for the selection or dynamic integration of the best predictors from a base of them to adapt to changing environments; whereas forecasting in volatile and complex environments is the main issue addressed; whereas Q-learning (QL), deep q network (DQN), double deep q network (DDQN), and deep deterministic policy gradient (DDPG) are the most widely used algorithms; and that, finally, the sectors of electric power, thermal energy, transport and telecommunications are the sectors where this type of forecast has been developed. Finally, given that all the models studied lack mechanisms for detecting concept drift, a new use of reinforcement learning for this purpose is proposed.
Optimizing turbine location in upgraded wind farm using grasshopper optimization algorithm
Authors: Nguyen Khoa Dang , Trung Tran Tinh , Vo Dieu Ngoc
Published: January 2025

This research explores the use of the grasshopper optimization algorithm (GOA) for optimizing the placement of additional turbines in an established wind farm. The primary objective is to increase the annual energy production (AEP) of the wind farm while minimizing the wake effects caused by both existing and new turbines. The research evaluates three different turbine types (1.5 MW, 2.0 MW, and 2.5 MW) to identify the most appropriate choice for increasing the wind farm's capacity. The GOA’s performance is compared with the commercial software windPRO and validated using WAsP software for energy calculations. Numerical results indicate that the GOA effectively improves wind farm layout, with the 1.5 MW turbines identified as the optimal choice for maximizing AEP and minimizing wake interactions. This study provides practical insights for wind farm operators and contributes to the development of advanced optimization techniques in wind energy.
Electromagnetic interference risk from electrostatic discharge in infant incubators
Authors: Trivida Elvina , Sudrajat Muhammad Imam , Ardiatna Wuwus , Prananto Haryo Dwi , Nugroho Hutomo Wahyu , Yoppy Yoppy , Anam Mohamad Khoirul , Bakti Aditia Nur , Mandaris Dwi , Arjadi R Harry
Published: January 2025

This paper proposes an improved electromagnetic compatibility (EMC) risk analysis approach for medical equipment related to the effect of electrostatic discharge (ESD). This approach not only focuses on the risk of ESD from the susceptibility aspect but also investigates its conducted electromagnetic interference (EMI) characteristics. This study combines the standardized ESD test and conducted emission (CE) measurement simultaneously, applying it to the infant incubator and analyzing the spectrum of ESD current in the phase line in the time and frequency domain. The result shows that an ESD exposure caused current spikes with an average level of 13.8 A. Moreover, it also causes a broad spectral CE noise on the phase line of the infant incubator. Furthermore, the CE noise in the low-frequency range was also detected on the phase line during ESD exposure, indicating the risk of interference with other sensitive medical equipment connected to the same power network. The approach of proposed risk analysis in this study can be used to identify the risks of EMI due to ESD events in implementing the latest IEC 60601-1-2.
Optimization of dynamic transmission network expansion planning using binary particle swarm optimization algorithm
Authors: Inyanga Faith Eseri , Muisyo Irene N. , Kaberere Keren K.
Published: January 2025

Increasing power demand is usually met by the expansion of generation capacity. The transmission network should be expanded in tandem to ensure power is evacuated from generation points to the load centres. Inadequate power capacity causes congestion. Congestion results due to under-voltages and violation of transmission lines’ loading limits. Constructing additional transmission lines is required to alleviate the congestion after measures of increasing the transmission line’s transfer capability are exploited. Transmission network expansion planning (TNEP) determines the transmission lines to be added to a power system at minimal construction cost, without violating network constraints. In this research, voltage limit violations are penalized in a constrained dynamic TNEP problem for a 10-year planning horizon. The optimal location and number of new transmission lines required at minimal construction cost, and transmission losses associated with the transmission network operations are determined. Improved binary particle swarm optimization (IBPSO) algorithm is applied to optimize the dynamic transmission network expansion planning (DTNEP) results. The developed model is tested on Garver’s 6-bus system using MATLAB. The construction cost for new transmission lines is minimized, and transmission losses reduced when compared to other published works without violating voltage limits (±5%) and transmission lines’ thermal capacities. The transmission network system adequacy is improved.
POA-DT: a novel method for predicting air quality in major Indian cities
Authors: Megavarnan Gayathri , Venkatachalam Kavitha
Published: January 2025

Air pollution is a critical environmental and public health concern, exacerbated by urbanization, industrial growth, and increased transportation. The air quality index (AQI) in major cities is significantly elevated due to rapid industrial expansion, fossil fuel consumption, and vehicular emissions. This study aims to predict AQIs using machine learning techniques, specifically integrating the Pelican optimization algorithm (POA) with the decision tree (DT) method to enhance accuracy. Data from prominent Indian cities—Mumbai, Delhi, Bangalore, Kolkata, and Chennai—was analyzed due to their high pollution levels. The model’s performance was validated against traditional machine learning methods such as k-nearest neighbors (KNN), random forest (RF) regression, and support vector regression (SVR). Results showed the highest prediction accuracies for Kolkata at 96.68%, followed by Bangalore at 95.66%, Chennai at 93.10%, Mumbai at 92.48%, and Delhi at 86.61%. These findings demonstrate that the proposed model outperforms conventional techniques in predicting AQI, providing a foundation for effective policy-making to mitigate air pollution impacts.
SentimentLP: unveiling advanced sentiment analysis through Leptotila optimization-based gradient boosting machines
Authors: Merlin Durairaj John Louis Anitha , Kumar Dhanasekaran Vimal
Published: January 2025

Sentiment analysis is pivotal in extracting insights from textual data, enabling organizations to understand customer opinions, market trends, and brand perception. This study introduces a novel approach, SentimentLP, which integrates Leptotila optimization (LPO) with gradient boosting machines (GBM) for sentiment analysis tasks. The proposed framework leverages LPO’s dynamic optimization capabilities to enhance GBM models’ performance in sentiment classification. Through iterative refinement and adaptive learning, SentimentLP optimizes feature extraction, model training, and ensemble learning processes, improving sentiment analysis accuracy and efficiency. Results from various evaluation metrics, including precision, recall, classification accuracy, and F-measure, demonstrate the effectiveness of SentimentLP in accurately capturing sentiment expressions in text data. Additionally, the fusion of LPO with GBM ensures scalability, adaptability, and interpretability of sentiment analysis models, making SentimentLP a valuable tool for extracting actionable insights from textual data across diverse domains and applications.
Environmental odor detection and classification with electronic nose system
Authors: Macías-Quijas Ricardo , Velázquez Ramiro , Del-Valle-Soto Carolina , Lizut Rafal , Visconti Paolo , Lay-Ekuakille Aimé
Published: January 2025

A prototype of an electronic nose (e-nose) system integrating a set of general-purpose gas sensors, an electronic module, and signal processing and classification methods has been designed and implemented to detect certain environmental odors that might pose a risk to human health. The proposed device explores the filter diagonalization method (FDM), an advanced signal processing technique for accurate spectral estimation, to detect the presence of odors together with random forest (RF), a popular machine learning algorithm, to classify the features of such spectra. Experimental results show that the proposed FDM-RF approach can recognize the targeted odors with an accuracy of 96.4%.
Develop a quantum key distribution application based on the BB84 protocol combined with a classical channel
Authors: Nguyen Tat-Thang , Dao Thanh-Toan , Luc Nhu-Quynh
Published: January 2025

Amid the escalating concerns over internet security, quantum cryptography stands out as a highly promising solution for significantly enhancing the security of networking systems, emerging among them is the quantum key distribution (QKD) with the function of creating secret session keys a breeze when leveraging the intriguing properties of quantum mechanics. This study is rooted in the BB84 QKD method, where in the distribution process in the quantum realm is simulated to derive a shared key via a public channel connecting two clients with the assistance of a server, utilizing the quantum inspire (QI) platform to generate qubits within the BB84 protocol. The results, the findings regarding the performance of BB84 reveal that when the server is set up, and the key size increases to 4000 bits, the process of sending module takes 16.215 sec, the transfer module takes approximately 5.2 hours, the receive module takes 1.257 sec to finish the process for the final session key share. This indicates a noteworthy enhancement in the execution speed of QKD employing the BB84 protocol, which now holds the potential for reinforcing network security using quantum computing systems.
Lung diseases identification using hybrid transfer learning and bidirectional long short-term memory
Authors: Eka Cahyani Denis , Tri Oktoviana Lucky , Yasin Mohamad , Wahyuningsih Sapti , Dionixius Dionixius , Maulidaningsih Ranti , Setumin Samsul
Published: January 2025

Lung diseases rank as the third most prevalent cause of mortality globally. Accurate identification of lung disease is essential to provide appropriate medical intervention for patients. This research devised a categorization system for lung diseases using chest X-Rays (CXR). The system can identify bacterial pneumonia, viral pneumonia, COVID-19, tuberculosis, and normal CXR. The approach for detecting lung diseases utilize a combination of hybrid transfer learning and bidirectional long short-term memory. The research included convolutional neural network (CNN) models including Resnet50-BiLSTM, VGG19-BiLSTM, InceptionV3-BiLSTM, Resnet50, VGG19, and InceptionV3. The Resnet50-BiLSTM model outperforms other models in terms of accuracy and overall performance. The Resnet50-BiLSTM model achieved an accuracy of 99.87%. The models that achieve the second greatest accuracy are Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, and InceptionV3. The research utilizes precision, recall, and F1-Measure to demonstrate that Resnet50-BiLSTM outperforms other methods by achieving the greatest value. This research improves the performance outcomes when compared to earlier studies.
Digital signal processing for accurate body temperature measurement
Authors: Koshekov Abay , Orazbayeva Bibigul , Savostin Alexey
Published: January 2025

The relevance of the subject matter is conditioned by the need to find the most effective and stable point for measuring body temperature using special measuring instruments. The main purpose of this scientific research is to develop a method for the most accurate implementation of these operations through the use of scientific and theoretical apparatus of digital signal processing. The study used methods of analysis and synthesis of information about the main provisions necessary in the process of finding an effective place for measuring the temperature parameter. The main points of body temperature measurement were established, including the key provisions of the digital temperature signal processing technique for measuring these parameters, which are required to assess the real state of a person. The presented mathematical formulas reflect the possibilities of calculating the average body temperature, as well as the skin temperature parameter, determined depending on the temperature indicators at individual points. Temperature curves for various types of diseases were presented. The practical significance of the results obtained lies in the possibility of their use in the activities of institutions of various profiles to find an effective method of measuring human body temperature to assess the physical condition.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection
Authors: Indriani Fatma , Budiman Irwan , Kartini Dwi , Handayani Lilies
Published: January 2025

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
Multimodal deep learning from sputum image segmentation to classify Mycobacterium tuberculosis using IUATLD assessment
Authors: Saurina Nia , Chamidah Nur , Rulaningtyas Riries , Aryati Aryati
Published: January 2025

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.
Strategic processor task allocation through game-theoretic modeling in distributed computing environments
Authors: Telmanov Merlan , Suchkov Mikhail , Abdiakhmetova Zukhra , Kartbayev Amandyk
Published: January 2025

This paper explores a game-theoretic model for task allocation in distributed systems, where processors with varying speeds and external load factors are considered strategic players. The goal is to understand the impact of processors' strategic behaviors on workload management and overall system efficiency, focusing on the attainment of a pure strategy Nash Equilibrium (NE). The research rigorously develops a formal mathematical model and validates it through extensive simulations, highlighting how NE ensures stability but may not always yield optimal system performance. The adaptive algorithms for dynamic task allocation are proposed to enhance efficiency in real-time processing environments. Results demonstrate that while NE provides stability, the adoption of optimal cooperative strategies significantly improves operational efficiency and reduces transaction costs. The findings contribute valuable insights into the strategic interactions within computational frameworks, offering guidelines for developing more efficient systems. This study not only advances the theoretical understanding of strategic task allocation but also has practical implications for system design and policy-making in areas such as cloud computing and traffic management.
Data-driven clustering of smart farming to optimize agricultural practices through machine learning
Authors: Thongnim Pattharaporn , Srinil Phaitoon , Pukseng Thanaphon
Published: January 2025

This study investigates the optimization of durian farming practices in Eastern Thailand using data-driven clustering techniques. The research aims to identify distinct agricultural patterns and improve resource allocation in durian production. K-means clustering is applied to durian production area and yield data from 2012 to 2023. Cluster quality is assessed using the Davies-Bouldin index (DBI), Dunn index, and Silhouette score. The methodology included comparing clustering results before and after log transformation of the data. Three main clusters are identified which are large-scale high-yield producers, small-scale lower-yield areas, and medium-scale producers with moderate yields. Notably, log transformation did not consistently improve clustering performance with original data often producing better-defined clusters. This finding highlights the importance of carefully considering data pre processing methods. Furthermore, the data-driven clustering offers valuable insights for precision agriculture by identifying regions with higher productivity allowing for targeted interventions and better resource allocation. The results can guide farmers in optimizing durian cultivation strategies, potentially leading to increased yields and more sustainable farming practices in Eastern Thailand's durian industry.
Discovering solutions of economic load dispatch problem by war strategy optimization algorithm
Authors: Nguyen Hung Duc , Pham Ly Huu
Published: January 2025

This paper suggested two methods, called war strategy optimization algorithm (WSO) and tunicate swarm algorithm (TSA), to find solutions for economic load dispatch problem (ELD). Various test systems with complex restrictions and discontinuous objective functions are used to assess the efficacy and resilience of the applied methods. The test cases are ranked from the simplest to the most complicated ones, in which the last with load demands are changed from the minimum power to the maximum power of total power of all units. The result comparison indicated that WSO can always reach the best cost for all test systems, but TSA cannot achieve similar values. Namely, WSO can reduced smaller cost than TSA by $ 0.565 and $ 121,325 for the first and second test systems, respectively. In comparison to other previous methods, the results found by WSO are equal to or better than those from others; however, the searchability of WSO is faster. Consequently, WSO is highly effective for handling ELD problem and can be considered for applying different problems in the engineering domain.
A reliable and secure demand side control for an IoT-enabled smart power system using machine learning
Authors: Harika Vemulapalli , Madhavi Gudavalli , Battu Hanumantha Rao , Kasukurthi Rambabu , Jangir Pradeep , Mesia Dhas John T
Published: January 2025

As the adoption of IoT-enabled smart power systems grows, the necessity for reliable and secure demand-side control becomes paramount. This paper introduces a robust demand-side management (DSM) engine that leverages machine learning to enhance both the reliability and security of smart grids. This paper presents a novel demand-side control system leveraging advanced machine learning techniques to optimize energy usage in smart power systems. The proposed system integrates IoT devices for data acquisition and employs machine learning algorithms to forecast energy demand, detect anomalies, and enable adaptive control strategies. By harnessing predictive analytics, the system anticipates consumption patterns and adjusts power distribution to maintain stability and prevent overloads. Moreover, robust security protocols are incorporated to protect the system against cyber threats and unauthorized access, ensuring data integrity and user privacy. Extensive simulation results demonstrate the system’s efficacy in reducing energy wastage, improving load balancing, and enhancing the overall reliability of the power grid. This research underscores the potential of combining IoT and machine learning to develop resilient and secure energy management solutions, paving the way for more sustainable and smart power systems.
Investigations related to effect of mass density of metal oxide on specific capacitance of asymmetrical supercapacitor
Authors: Bhajekar Mugdha , Karandikar Ankur , Joshi Sarang , Chaphekar Shalaka , Karandikar P. B.
Published: January 2025

Various types of batteries and fuel cells are widely used electrical energy storage devices in most of the applications. Supercapacitors have also become increasingly popular for energy storage in a few applications. Recently the need to develop better energy storage device has resulted in the development of asymmetrical supercapacitors (ASCs). Modeling of this device can be promising to obtain the desired characteristics to meet the need of continuous power and pulse current. For this, both positive and negative electrodes are important components for ASCs. The effect of mass density of metal oxide in positive electrode of this device is hardly researched. Component-based modeling for positive electrode is presented in the paper. Positive electrode parameters are being analyzed with the three most significant parameters affecting specific capacitance. The parameters viz. loading of materials on the current collector, percentage metal oxide, and mass density of metal oxide of the positive electrode are correlated with specific capacitance by using statistical methods to avoid the use of costly electrochemical methods. The major contribution of the presented research work is the identification of – mass density of metal oxide used in positive electrode plays the most significant role in deciding the value of the specific capacitance of this device.
Improving energy indicators of pulse converters
Authors: Dovudov Sarfaroz , Dunaev Mikhail , Zhiravetska Anastasia , Tavarov Saidjon , Yunusov Rustam , Safaraliev Murodbek
Published: January 2025

All rectifier circuits are divided into single-phase and three-phase, according to the number of phases of the supply network, single-cycle and two-cycle. Voltage conversion, which can vary in both frequency and amplitude, is carried out by two series-connected converters-a rectifier (AC/DC converter) and an inverter (DC/AC converter). Using simulation techniques in the MATLAB-based Simulink environment, the blocks used were taken from the sim power system/Simscape library. Models of semiconductor converters with pulse-width modulation based on one power thyristor switch and a semiconductor converter with pulse-frequency modulation based on four power thyristor switches have been developed. Experiments prove the correctness of the models. The results of a study of the developed models of semiconductor converters with pulse-width and pulse-frequency modulation are presented. The static and dynamic characteristics of pulsed semiconductor converters are presented. Analysis of the static characteristics of pulse converter circuits showed that the rigidity of the output characteristics of converters with pulse-frequency modulation is higher than that of converters with pulse-width modulation. The results of assessing the efficiency of pulsed semiconductor converters based on the analysis of static output characteristics allow us to conclude that the efficiency of a semiconductor converter with pulse-frequency modulation is more than one percent higher than that of a semiconductor converter with pulse-width modulation.
Analysis of FinFET based SRAM cells with improved performance parameters
Authors: Garg Deepak , Singh Rupali , Sharma Devendra Kumar
Published: January 2025

To resist the advanced process variation and enable ultra-low power operation, static random-access memory (SRAM) undergoes an expansion stage. The most common type of memory is SRAM, which occupy more than 60% of the chip area. All memories have occupied more than 80% of the circuit area in that day’s micro devices, and this trend is expected to continue. This paper develops into the deployment of SRAM using FinFET technology for implementation, with a primary objective of mitigating critical memory parameters, including parameters named as power dissipation, data retention and noise voltage. In this article, multiple simulations are carried out among conventional SRAM cells and FinFET based SRAM cells (6T, 7T, and 8T) utilizing the Cadence Virtuoso tool with a 45nm technology node. In modern era, FinFET is gaining increased preference over CMOS for high controllability of short-channel effects and flexible adjustment of threshold voltage (Vth) through the presence of a double gate. The thinner width of FinFET (Wfin) shows less degradation in performance in compared to thicker width of FET. To improve the circuit performance, the key factors like area, power and delay should be reduced. In the proposed SRAM cell using FinFET, power dissipation is lowered by 17% data retention voltage is reduced by 7% and noise voltage abridged up to 35% as compared to conventional SRAM cell.
Smart home advisory system based on IoT-enabled sensor network
Authors: Mohd Ashrap Khan Mohamed Feroz , How Thien Gregory Soon , Yap Boon Kar , Chan Kah-Yoong
Published: January 2025

A consistent drive to make daily living more streamlined and less complex was critical throughout the industrial revolution. Although many applications are available, the general adoption of these applications is hindered by several factors (security concerns, expensive costs, and perceived usefulness). Hence, a cost-efficient smart home system can provide significant advantages without placing unnecessary financial or operational costs on consumers. This study proposed an inexpensive internet of things (IoT) enabled smart home advisory system for promoting healthier living conditions. The system contained numerous sensors for detecting surrounding brightness, temperature, humidity, and dust levels. Consequently, the system demonstrated effective real-time updates regarding the simulated temperature (22–32 °C), humidity (11–53 %RH), light (3–100 lx), and dust (0–278 mg/m3) environments. Blynk software was also embedded as an efficient user interface for the application. Overall, the lowcost IoT-enabled smart home advisory system offered concrete benefits by allowing users to improve their living surroundings while decreasing energy usage.