Indonesian Journal of Electrical Engineering and Computer Science
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Spread of harmful substances in the atmosphere of industrial cities of Kazakhstan: modeling and data refinement
Get PDFIn Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health
Land scene classification using diversity promoting metric learning-convolutional neural network
Get PDFThe land scene classification by remote sensing images predicts semantic class of image blocks by removing visual primitives in remote sensing images. However, there is a problem of within-class diversity and between-class similarity that degrades a performance of scene classification. In this research, the diversity promoting metric learning–convolutional neural network (DPML-CNN) method is proposed for classifying land scene images. The metric learning with convolutional neural network (CNN) maps the same scene image class closer and the different class scenes as far as possible which makes the method much discrimination. The diversity promoting in metric learning is used to reduce the overlapping of the same scene class by uncorrelation of every parameter and provides unique information for those parameters. The UC Merced, AID, and NWPU RESISC45 datasets are utilized in this research for evaluating the proposed DPML-CNN method with evaluation metrics like accuracy and kappa coefficient. The DPML-CNN method reached highest accuracy of 99.27% and 99.84% for 50% and 80% training ratios on the UC Merced dataset when compared to other existing methods like multi-level semantic feature clustering attention (MLFC-Net) and global context spatial attention (GCSA-Net)
Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques
Get PDFLung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings
Comparative analysis of different types of pulse width modulation techniques for multilevel inverters
Get PDFMultilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation
An efficient method for privacy protection in big data analytics using oppositional fruit fly algorithm
Get PDFThis work employs anonymization techniques to safeguard privacy. Data plays a vital role in corporate decision-making in the current information-centric landscape. Various sectors, like banking and healthcare, gather confidential information on a daily basis. This information is disseminated by multiple sources through numerous methods. Securing sensitive data is of paramount importance for any data mining application. This study safeguarded confidential information using an anonymization technique. Several machine learning methodologies have a deficiency in accuracy. The study seeks to generate superior and more precise results compared to alternative methodologies. For large datasets, numerous solutions exhibit increased time complexity and memory use. For huge datasets, numerous solutions require more time and memory. The enhanced fuzzy C-means (FCM) algorithm surpasses existing approaches in terms of both accuracy and information preservation. This study provides a comprehensive analysis of data anonymization utilizing the oppositional fruit fly approach, a technique that enhances privacy. The clustering method being presented utilizes an enhanced version of the FCM algorithm. The secrecy of the recommended oppositional fruit fly algorithm is effective. The comparison demonstrated that the proposed research enhanced both accuracy and privacy in comparison to two existing methods. The existing strategy outperforms data anonymization-based privacy preservation by 82.17%, while the suggested method surpasses it by 94.17%
A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring
Get PDFThis research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches
Predicting autism spectrum disorder through sentiment analysis with attention mechanisms: a deep learning approach
Get PDFAutism spectrum disorder (ASD) is considered a spectrum disorder. The availability of technology to identify the characteristics of ASD will have major implications for clinicians. In this article, we present a new autism diagnosis method based on attention mechanisms for behavior modeling-based feature embedding along with aspect-based analysis for a better classification of ASD. The hybrid model comprises a convolutional neural network (CNN) architecture that integrates two bidirectional long short-term memory (BiLSTM) blocks, together with additional propagation techniques, for the purpose of classification the origins of Autism Tweet dataset; the proposed work takes Autism Tweet dataset and preprocesses them to employ n-gram to extract features of which the features of the ASD behavior are fed to generate the significant behavior for classification. The model takes into account both behavior-guided features across every aspect of the Class/ASD to provide higher accuracy using Adam optimizer. The experimental values inferred that the n-BiLSTM technique reaches maximum accuracy with 98%
A framework for dynamic monitoring of distributed systems featuring adaptive security
Get PDFDistributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment
Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions
Get PDFThe integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances
Machine identification codes of color laser printers: revisiting privacy and security
Get PDFForging legal documents has been easier and faster with the advancement of technology. Printer identification has become a critical field for tracing criminals and validating the authenticity of documents. The current study uses a non-destructive method to detect and identify covert embedded hidden information (machine identification codes (MIC)). Samples were collected from popular brands, including Xerox and HP color laser printers, to attain this aim. Their printouts were then scanned at 600 dpi using a Konica Minolta scanner. Scanned images were subjected to graphic editors for linear and non-linear adjustments. Following this, yellow-toner dots were observed as a base pattern. Grayscale imaging with a computational approach to analyze the yellow dot patterns was utilized for intensity-focused analysis, with edge detection algorithms applied using Python to enhance and highlight the converted patterns in printed documents. The printouts from Xerox printers exhibited repeating patterns. However, no such detailed information was observed in prints from HP printers, even when analyzed using binary code for deductions. A notable variation was detected in the yellow tracking dots among both brands, which can be instrumental in identifying the origin of printouts and scanned images for forensic investigations. This methodology provides conclusive and dependable accuracy