Indonesian Journal of Electrical Engineering and Computer Science
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Navigating the smart contract threat landscape: a systematic review
Get PDFSmart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users
Two RC model and parameter estimation of lithium-ion battery
Get PDFElectric vehicles are the trend of this decade. Frequent high-power requirements of electric vehicles, make the batteries discharge at higher C rate. Discharging at a higher C rate will lead to higher heat production leading to destruction and explosion of the battery. To optimize the charging and discharging C rates considering both safety and performance factors, battery management system (BMS) is used as an eternal component of power source. To estimate the state of charge (SOC), which is an essential component of BMS, accurate battery modelling is required. Two RC model is one of the most used lithium-ion battery model, due to its simplicity and accuracy. The equivalent circuit parameters, resistances and capacitance do change with SOC and temperatures. This paper focuses on estimation of equivalent circuit parameters for a wide range of temperatures and SOCs ranging from -20 degree celsius to +25 degree celsius and 100 to 0 respectively. We have developed two RC model for Panasonic 18650PF and estimated the parameters of the model using hybrid pulse power characterization (HPPC) data. MATLAB based parameter estimator is used in determining the equivalent circuit parameters
Convolutional neural network for speech emotion recognition in the Moroccan Arabic dialect language
Get PDFExtracting the speaker's emotional state has become an active research topic lately due to the demand for more human interactive applications. This field of research has noted significant advancement, especially in the English language, owing to the availability of massive speech-labeled corpora. However, the progress of analogous methodologies in the Arabic language is still in its infancy stages. This paper presents a new massive natural speech emotion dataset and a speech recognition model for the Moroccan Arabic language. Four primary emotion labels were selected: happy, sad, angry, and neutral. Various spectral features, such as the mel-frequency cepstral coefficient (MFCC), were extracted and tested to determine the optimal feature combination. A convolutional neural networks (CNNs) model was built and trained on our dataset. The results were compared between spectral features individually and combined with the CNN model resulting in the selection of MFCC, root-mean-square (RMS), mel-scaled spectrogram, and spectral, as optimal spectral features for our dataset. This selection yielded significant results, with an accuracy of 99.55% for emotion recognition, outperforming the existing research
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Get PDFDark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP
Computer simulation of transition modes in flow reactors considering the multistage and reactions non-perfectness
Get PDFDue to the variety of reaction types and schemes in chemical-technological apparatuses, a general engineering methodology to assessing how the transient modes and reactions multi-stage act the kinetics in conditions of occurrence of moving reaction fronts in flow apparatuses has not yet been developed. The paper devotes to constructing the mathematical models for several important cases of the problem mentioned, namely: for theoretically study the kinetic dynamics of the conversion process in a three-stage chemical reaction with an autocatalytic first stage and the presence of a mass source of one of the components. An original mathematical model for describing the chemisorption dynamics at the initial stage of forming a moving reaction front in flow-through apparatuses has been developed. A special algorithm and numerical solution for the initial absorption period have been constructed, and appropriate computer simulation has been implemented. The significant influence of multistage on the formation and on stability types of stationary states has been established. Expressions to evaluate the characteristics of the emerging oscillatory modes have been obtained too. The results can be used to assess the influence of control parameters on the reaction front movement speed
Performance enhancement of a terahertz patch antenna with metamaterials for 6G and biomedical applications
Get PDFThis paper introduces a novel approach for enhancing the performance of a terahertz (THz) patch antenna through the integration of metamaterials (MTM). The proposed design features a rectangular slotted patch antenna with a partial ground structure (DGS) that operates at 3.56 THz. The radiating element is situated on a substrate composed of silicon dioxide (SiO2) with a dielectric of 4 and a thickness of 2 µm. The proposed MTM is a 6×5 elements with a FR4 substrate characterized by a dielectric of 4.2 and a thickness of 2 µm. The MTM is integrated beneath the antenna as a strategic technique to enhance its performance. The results confirm the significant impact of this integration. The MTM improves impedance matching and makes the antenna more directional. Consequently, the reflection coefficient is improved from -18.06 dB to -52.50 dB, the gain is increased from 1.72 dB to 3.49 dB, and the directivity also is enhanced from 3.69 dB to 5.10 dB. All results were obtained using HFSS software
Assessing fingerprinting and machine learning approaches for wireless indoor localization
Get PDFThis paper presents a comparative analysis of fingerprinting and machine learning techniques for bluetooth low energy (BLE)-based localization. Two fingerprinting algorithms, namely fingerprint feature extraction (FPFE) and Bayesian estimation (BE), along with various machine learning approaches including support vector regression (SVR), ensemble learning, and instance-based learning, are investigated. The selection of techniques depends on the availability of training data or the fingerprint database, explored in both ideal scenario and real-world scenario. In ideal scenario where the system administrator can collect fingerprint data through users’ devices, FPFE emerges as the preferred algorithm, achieving superior performance with a mean error of 0.50 m. In the context of real-world scenario, where data collection from multiple devices is limited, the system administrator may gather fingerprint data for localization using one or a few specific devices. Our experiments reveal that when there is a scarcity of fingerprint data, BE and SVR exhibit acceptable performance, reaching a mean error of 1.785 m and 1.965 m, respectively
Systematic review for attack tactics, privacy, and safety models in big data systems
Get PDFThis systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era
Development of clustering with Bayesian algorithm for optimal route formation in software-defined radio underwater WSN
Get PDFUnderwater wireless sensor networks (UWSNs) have recently offered chances to investigate oceans and thus enhance the underwater world. WSNs are imperative for discovering the ocean region. Software-defined networking (SDN) improves flexibility and uses the clustering method to improve lifespan. This article introduces the Development of a clustering process with a Bayesian algorithm (CPBA) for optimal route formation in software-defined radio UWSN. The clustering concept improves energy efficiency; however, cluster head (CH) selection is challenging. The present clustering mechanisms could be more successful in suitably assigning the node's energy. This mechanism utilizes a slap swarm optimization algorithm to pick out the optimal CH by node energy and distance among inter-cluster as well as intra-cluster. In addition, the Bayesian algorithm selects the best forwarder from sender to base station. Thus, enhances efficiency. The simulation results demonstrate that the UWSN improves both the 23% packet forward ratio and 0.014 joule energy. Furthermore, it minimizes the 30% network delay
Bibliometric analysis of model vehicle routing problem in logistics delivery
Get PDFThis bibliometric analysis focuses on the vehicle routing problem (VRP) model in the field of logistics delivery. The study utilizes a comprehensive dataset of 2,000 VRP-related publications obtained from the Scopus database, spanning the years 2007 to 2023. Through the application of bibliometric methods, this research aims to uncover key insights regarding research trends, country contributions, and recent topics within the VRP research network. Various bibliometric indicators, including publication count, author productivity, relevant sources, institutional affiliation, and citation frequency, are employed to conduct the analysis. The findings shed light on the evolution and trajectory of VRP research, while also highlighting noteworthy countries and topics that have received significant attention. This study not only enhances the overall understanding of VRP but also serves as a foundation for future investigations aimed at enhancing the efficiency and effectiveness of logistics delivery