International Journal of Informatics and Communication Technology (IJ-ICT)
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Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications
Get PDFThis article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB
Quantifying the severity of cyber attack patterns using complex networks
Get PDFThis work quantifies the severity and likelihood of cyberattacks using complex network modelling. A dataset from common attack pattern enumerations and classifications (CAPEC) is collected and formalized as nodes and edges aiming at creating a network model. In this model, each attack pattern is represented as a node, and an edge is created between two nodes when there is a relation between them. The dataset includes 559 attack patterns and 1921 relations among them. Network metrics are used to perform the analysis on the network level and node level. Moreover, a rank of the CAPECs based on a complex network perspective is generated. This rank is compared with the CAPEC ranking system and deeply discussed based on cybersecurity perspective. The findings show interesting facts about the likelihood and severity of attacks. It is found that the network perspective should be given attention by the CAPEC ranking system. Finally, the results of this work can be of high interest to security architects
Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research
Get PDFCloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration
Conceptualization of IoT architectures
Get PDFAlthough there is a large interest about internet of things (IoT) architectures, still there is no consensus on their conceptualization in the extant literature. This lack of information in conceptualization is problematic because it hampers the deep understanding of the appeared proposals, as well as the adoption of a shared workflow by the involved architects of these systems. Thus, a concise and agreed-upon conceptualization of IoT architectures is called for. This paper aims at giving a contribution on the topic. We start by reviewing the available standards, then, in light of their suggestions, a workflow to be followed in the definition of the architecture descriptions (ADs) of IoT systems is detailed and, in addition, a sample case study, which implements that workflow, is proposed. The contributions are sufficiently abstract to be applicable also to the description of the architecture of artificial intelligence of things (AIoT) systems
Data analysis and visualization on titanic and student’s performance datasets-an exploratory study
Get PDFExploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset
Automatic vehicle accident detection and alerting notification using internet of things
Get PDFImmigrants in developing countries have indirectly encouraged increased automobile use, leading to a strong association between automobile accidents and their victims. However, recent technological developments, especially artificial intelligence and electronics, seem promising in overcoming these risks. This research paper focuses on complex systems developed using internet of things (IoT) technology. The system integrates various components such as micro controller, radio frequency identification (RFID) card reader for license validation, liquid crystal display (LCD), Ultrasonic sensor for interference, measuring device and global positioning system (GPS) unit. Additionally, the system has a simple mail transfer protocol (SMTP) server that can send timely email alerts to emergency responds and log email addresses for real-time emergency detection. This facilitates rapid response and emergency rescue, thereby reduces the risk of accidents and increases overall safety
Analyzing radicalism sentiments in Indonesian da’wah content on website da’wah through text mining techniques
Get PDFThis study investigates the classification of radical content in Indonesian Da’wah websites using text mining techniques. A content search engine application, developed with PHP, processes queries by comparing results against a database of keywords, classifying content into four categories: red, yellow, green, and white. Manual labeling based on data from the Ministry of Communication and Informatics yielded 126 labeled articles, forming the dataset for classification. The K-nearest neighbors (K-NN) algorithm, with an optimal k value of 7, achieved a classification accuracy of 66.37%, demonstrating its reliability compared to manual methods. The “White” class showed the highest precision and recall. System testing revealed efficient performance, with 0.704 seconds per classification task and 884,656 bytes of memory usage. Future enhancements include incorporating synonym identification for Indonesian keywords and exploring machine learning algorithms such as Naive Bayes and neural networks to improve accuracy. This research highlights the potential for text mining in identifying online radical content while emphasizing the need for system adaptability
Smart hybrid power management system in electric vehicle for efficient resource utilization with ANN
Get PDFThe novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition
Pioneering the digital readiness for Malaysian museums: custom framework
Get PDFA museum is a hub for public exploration and education of community or country culture and traditions. Digital technologies transform museums into interactive experiences, engaging visitors and bringing cultural values to life. However, Malaysian museums struggle to adopt digital technologies due to limited infrastructure, expertise, exhibition technology, and budgets. These constraints hinder effective audience engagement and limit growth and modernisation efforts. To help Malaysian museums in digitalisation, this study aims to contextualise a digital readiness index (DRI) questionnaire. The findings of this pioneering study have yielded a unique and customised version of the DRI questionnaire specifically designed for Malaysian museums, marking the first-ever initiative of its kind in the country. The DRI serves as a pivotal scale or tool for managers and researchers, facilitating the evaluation and validation of a museum’s digitalisation status while guiding strategic planning for future advancements. This questionnaire enables researchers and museum managers to gain insights into the museums and understand which dimensions require focus and enhancement to ensure a successful and comprehensive transition towards digital transformation
Multilingual hate speech detection using deep learning
Get PDFThe rise of social media has enabled public expression but also fueled the spread of hate speech, contributing to social tensions and potential violence. Natural language processing (NLP), particularly text classification, has become essential for detecting hate speech. This study develops a hate speech detection model on Twitter using FastText with bidirectional long short-term memory (Bi-LSTM) and explores multilingual bidirectional encoder representations from transformers (M-BERT) for handling diverse languages. Data augmentation techniques-including easy data augmentation (EDA) methods, back translation, and generative adversarial networks (GANs)-are employed to enhance classification, especially for imbalanced datasets. Results show that data augmentation significantly boosts performance. The highest F1-scores are achieved by random insertion for Indonesian (F1-score: 0.889, Accuracy: 0.879), synonym replacement for English (F1-score: 0.872, Accuracy: 0.831), and random deletion for German (F1-score: 0.853, Accuracy: 0.830) with the FastText + Bi-LSTM model. The M-BERT model performs best with random deletion for Indonesian (F1-score: 0.898, Accuracy: 0.880), random swap for English (F1 score: 0.870, Accuracy: 0.866), and random deletion for German (F1-score: 0.662, Accuracy: 0.858). These findings underscore that data augmentation effectiveness varies by language and model. This research supports efforts to mitigate hate speech’s impact on social media by advancing multilingual detection capabilities