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31,456 Article Results

Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 diabetes

10.11591/ijai.v13.i4.pp4453-4471
Sachin Ramnath Gaikwad , Seeta Devi , Sameer Shekhar , Dipali Dumbre
Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.
Volume: 13
Issue: 4
Page: 4453-4471
Publish at: 2024-12-01

Predicting levels of legal case difficulties using machine learning

10.11591/ijai.v13.i4.pp4364-4371
Ilmiyati Sari , Rifki Kosasih , Dina Indarti
Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.
Volume: 13
Issue: 4
Page: 4364-4371
Publish at: 2024-12-01

Empowering industry through energy auditing: a case study of savings and sustainability

10.11591/ijape.v13.i4.pp952-962
Kamal Anoune , Mohamed Ghazi , Mokhtar Ghazouani , Badr Nasiri , Otmane Zebraoui
Conducting energy audits is pivotal in assessing industrial plant efficiency and formulating effective energy management plans. It identifies opportunities for efficient energy use, reducing costs and environmental impact. This study employs a techno-economic approach to analyze electricity cost reduction in an industrial facility. Through energy auditing, it explores economic benefits and improved energy quality, yielding favorable outcomes. Focused on a plastic derivative manufacturing plant, the study reveals critical audit findings. The main aim is to identify avenues for electric energy savings, cutting production costs, and enhancing product competitiveness. The audit involves a detailed analysis of consumption patterns, signal quality, and potential energy management strategies, culminating in a cost-cutting plan. The results of an economic assessment of the suggested energy-saving strategies, provide a comprehensive evaluation of their financial implications. It reveals significant cost reduction opportunities, estimating annual energy savings of $45,824.56, which represents a 23.68% decrease in expenses. These initiatives not only boost the plant's financial performance but also strengthen its competitive edge.
Volume: 13
Issue: 4
Page: 952-962
Publish at: 2024-12-01

Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture

10.11591/ijai.v13.i4.pp3987-4002
Javed Subhedar , Mrinal R. Bachute , Ketan Kotecha
In autonomous driving systems, the semantic segmentation task involves scene partition into numerous expressive portions by classifying and labelling every image pixel for semantics. The algorithm used for semantic segmentation has a vital role in autonomous driving architecture. This paper's main contribution is optimising the semantic segmentation algorithm for autonomous driving by modifying the U-NET architecture. The optimisation techniques involve five different methods, which include; no batch normalisation network, with batch normalisation network, network with reduction in filters, average ensemble network, and weighted average ensemble network. The validation accuracy observed for the five methods were 90.28%, 91.68%, 89.80%, 92.04%, and 92.21% respectively. By reducing the filters in the network, the computation time reduces (Epoch time: 1 s 64 ms/step) as opposed to the typical (Epoch time: 4 s 260 ms/step), but the accuracy reduces. The optimisation techniques were evaluated for metrics like mean intersection over union (IoU), IoU for class, dice-metric, dice_coefficient_loss, validation loss, and accuracy. The dataset of 300 images used for this paper's study was generated using the open-source car learning to act (CARLA) simulator platform.
Volume: 13
Issue: 4
Page: 3987-4002
Publish at: 2024-12-01

Patient safety culture as a mediator between healthcare personnel's psychological empowerment and patient outcome

10.11591/ijphs.v13i4.23436
Eldaa Prisca Refianti Sutanto , Ferdi Antonio , Roy G.A. Massie
Preventing healthcare errors and reducing damage are becoming the basic concepts of patient safety development. Healthcare personnel need to be more careful and minimize complications that can occur during patient care. Psychological empowerment is important to support a safety culture and patient outcomes. This study investigates the relationship between psychological empowerment (PE) and patient outcome (PO) mediated by patient safety culture (PSC). A cross-sectional survey study was conducted in private hospitals accredited by Joint Commission International. A total of 150 healthcare personnel as participants who met the requirements were obtained by distributing the questionnaires in March 2023. Significant results in mediating patient safety culture have been analyzed using partial least square-structural equation modeling (SEM). The dimensionality of PE and PSC was assessed by a new method that is a disjoint two-stage approach. The role of PSC was found to fully mediate the positive relationship between psychological empowerment and patient outcome (=0.436, p-value 0.05). This study contributes to growing the value of developing a safety culture and the function of psychological empowerment in healthcare staff to enhance patient outcomes.
Volume: 13
Issue: 4
Page: 1912-1923
Publish at: 2024-12-01

Design and development of knee rehabilitation robot

10.11591/ijra.v13i4.pp423-431
Devashish Sharma , Sahil Ahamed Shaik , Saran Rajagopal , Manju Mohan , Kuppan Chetty Ramanathan , Pavan Kalyan Lingampally
This research presents a comprehensive design and analysis of a knee rehabilitation platform aimed at aiding individuals with knee dysfunction. Dysfunction in the knee joint can lead to an imbalance in gait and posture during activities of daily living (ADLs) such as standing, walking, and running. This study focuses on developing a 2-degree-of-freedom (2-DoF) knee rehabilitation device capable of mimicking linear and angular movements. A slider mechanism-based knee rehabilitation device is developed and simulated alongside various other mechanisms. The proposed mechanism achieves 32.5° of flexion for a linear movement of 0.45 m within 6 seconds, outperforming other mechanisms. To validate simulation results, a 3D-printed model is fabricated, and experimental studies are conducted under no-load conditions, showing close alignment with simulation outcomes with a deviation of ±5%. The device’s key features include portability, compliance, compactness, and enhanced stiffness. Future research will involve conducting pilot studies to further evaluate the practical efficacy and potential enhancements of the proposed knee rehabilitation platform.
Volume: 13
Issue: 4
Page: 423-431
Publish at: 2024-12-01

Switching state vector selection for improved THD performance in multilevel current fed inverters

10.11591/ijpeds.v15.i4.pp2296-2307
G. Prem Kumar Reddy , Bhukya Mangu
This paper aims to explore synthesis methods of three-phase multi- level current waveforms by utilizing a configuration of n parallel operating three-phase, three-level current-fed inverters (CFIs). It points out an issue where differences in the DC-link current between inverters cause a distorted space vector diagram and decreased switching state redundancy, this leads to generation of low frequency harmonics in the output current and increased total harmonic distortion (THD). The research suggests a method that chooses the best switching state vectors by considering their closeness to a reference current space vector. This approach aims to decrease switching losses and low-frequency harmonic content. This method improves the quality of the waveform and the efficiency of the inverter by closely matching the switching operations with the intended current path. An analytical model is created to investigate how changes in DC-link current impact inverter performance. Simulations and actual tests confirm the ef-ficacy of the proposed strategy in enhancing total harmonic distortion (THD) and waveform accuracy. The results provide valuable information on improving multilevel inverter designs to enhance power quality and operational efficiency, indicating potential areas for further research in power electronics.
Volume: 15
Issue: 4
Page: 2296-2307
Publish at: 2024-12-01

A systematic review on voltage stability analysis in smart and islanded microgrid parallel inverters

10.11591/ijpeds.v15.i4.pp2422-2434
Sowmya Renuka Bangari , Bangalore Gangadharaiah Shivaleelavathi
The development of a smart grid depends on measuring and communication technologies and gathering heterogeneous data from micro grid. This enormous data contains procedures and methods that make it easier to use vast data while also increasing computing complexity and security. Given the maturity of power electronics technology, the penetration of distributed generation in both on- and off-grid environments has advanced in ways that have never been seen. Any complicated power system, like the off-grid parallel inverters, requires a stability study that considers several state variables. The smart grid environment's rising complexity and data explosion necessitate more hardware, which raises costs and necessitates additional physical space. An introduction to the special issue on voltage stability of micro grids with parallel inverters in power systems is presented in this review paper. Self-synchronizing inverters known as parallel inverters do not require a reference from the main grid to synchronize with one another. There are several general analytical techniques and enhanced procedures covered. The similarities, functionality, applications, designs, benefits, drawbacks, and general effectiveness are then contrasted. The significant contribution of this in-depth review is to establish a strong foundation for subsequent research in the area.
Volume: 15
Issue: 4
Page: 2422-2434
Publish at: 2024-12-01

Low-cost multi-sensing fire-fighting robot with obstacle avoidance mechanism

10.11591/ijra.v13i4.pp373-379
Adekunle Taofeek Oyelami , Joshua Ayomide Oyadokun , Olusola A. Akintunlaji , George C. Ihenacho
Robots are mostly optimized for tasks that require strength exceeding that of humans or for operations in hazardous environments. The fire-fighting robot developed has multiple sensing capabilities with obstacle avoidance mechanisms and is divided into two units: the robot and the static unit. The robot is equipped with three flame sensors to detect flames (infrared radiation) in three directions, an ultrasonic sensor to avoid obstacles, a wireless receiver to receive data from the static unit, a magnetometer giving the robot a sense of direction, and a unit of Arduino Mega microcontroller serving as the central controlling platform. The static unit has four flame sensors and a transmitter that transmits signals to the robot unit, which an Arduino Uno directly controls. A prototype was developed, which helps prevent the escalation of fires in the home as it can detect, navigate and extinguish flames while avoiding obstacles autonomously.
Volume: 13
Issue: 4
Page: 373-379
Publish at: 2024-12-01

The future of artificial intelligence-driven robotics: applications and implications

10.11591/ijra.v13i4.pp361-372
Tole Sutikno
Artificial intelligence (AI)-driven robotics is a rapidly evolving field that is transforming various industries, including healthcare, manufacturing, transportation, logistics, security, retail, agri-food, and construction. The integration of artificial intelligence algorithms and machine learning techniques has propelled robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and AI, with AI-driven autonomous cars, drones, and robots transforming transportation, healthcare, and exploration. It offers flexibility and learning capabilities, transforming the way machines interact with humans. The integration of AI and robotics marks a transformative era in which machines become companions and cognitive extensions of human capabilities. In the future, we expect AI-driven robotics to bring significant changes to employment and societal well-being. However, the development of AI-driven robotics, which is the integration of AI and robotics, faces numerous challenges, including ethical concerns, legal issues, regulations, societal implications, and job market impacts for the proliferation of intelligent machines. Furthermore, it also presents challenges in terms of technical complexities in its development.
Volume: 13
Issue: 4
Page: 361-372
Publish at: 2024-12-01

Methodology applied to computer audit with artificial intelligence: a systematic review

10.11591/ijai.v13.i4.pp3727-3738
Sheyla Reymundez Suarez , Bryan Martínez Huamani , María Acuña Meléndez , Christian Ovalle
This systematic review focused on evaluating the impact of the machine learning operations (MLOps) methodology on anomaly detection and the integration of artificial intelligence (AI) projects in computer auditing. Data collection was carried out by searching for articles in databases, such as Scopus and PubMed, covering the period from 2018 to 2024. The rigorous application of the preferred reporting items for systematic reviews and metaanalyses (PRISMA) methodology allowed 88 significant records to be selected from an initial set of 1,389, highlighting the completeness of the selection phase. Both quantitative and qualitative analysis of the data obtained revealed emerging trends in the research and provided key insights into the implementation of MLOps in AI projects, especially in response to increasing complexity, whereby the adoption of the MLOps methodology stands out as a crucial component to optimize anomaly detection and improve integration in the context of information technology auditing. This systematic approach not only consolidates current knowledge but also stands as an essential guide for researchers and practitioners, and the information derived from this systematic review provides valuable guidance for future practices and decisions at the intersection of AI and information technology auditing.
Volume: 13
Issue: 4
Page: 3727-3738
Publish at: 2024-12-01

Vision-based approach for human motion detection and smart appliance control

10.11591/ijra.v13i4.pp445-451
Siddharth Swami , Rajesh Singh , Anita Gehlot , Mohammed Ismail Iqbal , Sameer Dev Sharma , Dharmendra Kumar , Sanjeev Kumar Shah
This study focuses on the use of computer vision technology and motion detection sensors to create an intelligent system that recognizes human presence in monitored spaces. The system uses a relay module for automation and control of household appliances while sensing motion detection, operated by an ESP32 microcontroller. This innovative solution addresses two major issues in home automation: reliable human presence recognition and seamless appliance control. The research merges a camera-based vision system with motion sensors, comparing motion and vision-based identification. The ESP32 microcontroller improves motion detection precision and context awareness by integrating motion sensors and computer vision technologies. The integration of a camera module allows real-time analysis and recognition of human presence, reducing false alarms. The relay module also enables automated control of home appliances, synchronizing and feedbacking operations with sensed human presence. The dynamic adaptation of the system improves user convenience and energy efficiency.
Volume: 13
Issue: 4
Page: 445-451
Publish at: 2024-12-01

Accuracy based-stacked ensemble learning model for the prediction of coronary heart disease

10.11591/ijai.v13.i4.pp4516-4525
Santosini Bhutia , Bichitrananda Patra , Mitrabinda Ray
Coronary heart disease (CHD) is the primary cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Medical researchers use machine learning techniques to predict CHD. This article proposes an accuracy based-stacked ensemble learning (AB-SEL) model to predict CHD while minimizing computational time (CT). The dataset undergoes the logistic regression recursive feature elimination (LR-RFE) method to identify the important features. The three strong classifiers, logistic regression (LR), random forest (RF), and AdaBoost, are chosen to build ensemble machine-learning models, including techniques like bagging, majority voting, and stacking, for the Cleveland dataset accessible from Kaggle. Data scaling was done using the normal scalar method, and hyperparameter optimization was done using random search and grid search. Effectiveness is measured by accuracy, precision, recall, F1 score, and CT is validated through 5-fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, required only 0.2 seconds of CT, and yielded an area under the curve (AUC) of 0.892.
Volume: 13
Issue: 4
Page: 4516-4525
Publish at: 2024-12-01

Design comparison of surface-mounted permanent magnet synchronous motors with inner and outer rotor configurations

10.11591/ijpeds.v15.i4.pp2105-2114
Hoang Bui Huu , Bao Doan Thanh , Tran Duc Chuyen , Vuong Dang Quoc
The surface-mounted permanent magnet synchronous motor (SPMSM) is one of the electric machines applied widely in the fields of electric vehicles (EVs) and electrical drives due to their good characteristics such as high power density, lower mass, high efficiency, and lower torque. For the SPMSM, there are two types of SPMSM, i.e., the inner rotor SPMSM and the outer rotor SPMSM. To analyze and compute advantages and disadvantages, as well as to compare the performances of these two motor types, this research proposes an analytical model to design preliminarily the main/required parameters of the SPMSM with inner and outer rotor types. Subsequently, a finite element method is developed to simulate, analyze, and compare the electromagnetic parameters of the proposed motors. Via the developed methods, the obtained results will also indicate the performances of both types of motors. In particular, it will provide a good recommendation for choosing the SPMSM with an inner or outer rotor structure for traction applications.
Volume: 15
Issue: 4
Page: 2105-2114
Publish at: 2024-12-01

Digitally fast synchronization of single-phase grid-tied inverter using FPGA

10.11591/ijpeds.v15.i4.pp2452-2461
Afarulrazi Abu Bakar , Balarajan Sannasy , Hazwaj Mhd Poad , Tharnisha Sithananthan , Wahyu Mulyo Utomo , Nor Farisha Diana Rosli
As interest in alternative energy sources grows, grid-connected inverters are getting more advanced. Thus, to synchronize the output waveform of an inverter with the grid supply system, the frequency and phase angle ought to be consistent. This paper presents an enhanced digital implementation controller for a grid-connected inverter using the sinusoidal pulse-width modulation (SPWM) switching technique via an appropriately designed low-pass filter. The main contribution of the proposed digital controller algorithm is to synchronize with the grid for the next half-cycle. The proposed control technique used the integrated response from the zero-crossing detector (ZCD) circuit for every half-cycle to trigger the digital phase-locked loop (PLL) implemented using field-programmable gate array (FPGA). An experimental 100 W single-phase full-bridge inverter prototype tested and validated the proposed control algorithm to prove the switching approach works. From the experimental results, the proposed control algorithm demonstrates synchronization with the grid voltage within 8 ms during startup. Furthermore, it exhibits the ability to adapt to phase changes when subjected to a distorted grid, achieving synchronization within 42 ms. This research also emphasizes synchronization between the grid waveform and the inverter output via the phase angle difference.
Volume: 15
Issue: 4
Page: 2452-2461
Publish at: 2024-12-01
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