Indonesian Journal of Electrical Engineering and Computer Science
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MoBiSafe: an obfuscated single factor authentication mode to enhance secured USSD channel transaction in Nigeria
Get PDFThe flexibility of the unstructured supplementary service data (USSD) across mobile phones has caused its adoption surge as a payment channel. Its usage accommodates financial inclusivity and extends customer reach irrespective of their specific phone capabilities. With data conveyed on the USSD channel in plaintext–this has raised vulnerability issues with shoulder surfing attacks. The use of password yielded extra layer of security as authentication to USSD-based services. But, the rise in password guess attacks has necessitated a new scheme. This study is a randomized-obfuscated single factor authentication (SFA) mode via a 5-digit PIN-entry as requisite for the USSD channel. It yields a list via which users select a key-array that corresponds to their PIN as concealed in a 10-digit array. Expert assess of MoBiSafe’s usability and security against shoulder-surf yielded 10.1 msecs and 2.26 msecs respectively to outperform existing models that utilize direct/indirect PIN-entry as in USSD transactions. And this was found to be both secure, usable and acceptable
Designing an automated matching model to enhance recruitment process
Get PDFDetecting qualified candidates for a vacant position is a difficult task, especially when there are numerous applicants. This delays team development in finding the appropriate individual at the right moment. Adopting a well-structured selection process will create opportunities for new aspects and ideas. In this paper, the matching job applicant (MJA) model is developed to assist all parties, the employers and the employees simultaneously by providing a fair, transparent unbiased solution constructed by using a mathematical machine. This provides a clear justification in the decision-making process in addition to advising the applicants with the most suitable positions that fits their qualifications
Texture-based two-stage shot boundary detection in videos
Get PDFIn recent years, shot boundary detection (SBD) has become an essential component of video processing, enabling applications such as video indexing, summarization, and content retrieval. However, the task remains challenging due to frequent false positive detections caused by illumination variations, motion changes, and diverse editing effects. To address these challenges, this paper presents a novel two-stage SBD framework that leverages local quad pattern (LQP) histogram features for precise transition detection. In the first stage, histogram feature vectors are derived by counting the occurrences of LQP codes (−1, +1, 1, 0), and abrupt transitions are identified using the Euclidean distance between consecutive frames. In the second stage, mean values of each histogram bin are computed for consecutive frames, and a similar distance-based approach is applied to refine detection accuracy. A transition frame is confirmed as a shot boundary only if both stages detect it, thereby reducing false positives. The proposed method is evaluated on the TRECVid 2001 and 2007 benchmark datasets, and experimental results demonstrate its superior performance compared to existing algorithms
Early detection of food safety risks using BERT and large language models
Get PDFSentiment analysis can be a powerful tool in safeguarding public health. This allows authorities to investigate and take action before a foodborne illness outbreak spreads. This paper introduces a novel system that proactively empowers restaurants to identify potential food safety hazards and hygiene regulation violations. The system leverages the power of natural language processing (NLP) to analyze Arabic restaurant reviews left by customers. By fine-tuning a pre-trained BERT mini-Arabic model on three targeted datasets: Sentiment Twitter Corpus, an Algerian dialect dataset, and an Arabic restaurant dataset, the system achieves an impressive accuracy of 91%. Additionally, the system caters to spoken feedback by accepting audio reviews. We utilized Whisper AI for accurate text transcription, followed by classification using a fine-tuned Gemini model from Google on Algerian local comments and others generated using large language models (LLMs) through few-shot learning techniques, reaching an accuracy of 93%. Notably, both models operate independently and concurrently. Leveraging RESTful APIs, the system integrates the solved sub-solutions from each microservice into a fusion layer for a comprehensive restaurant evaluation. This multifaceted approach delivers remarkable results for both modern standard Arabic (MSA) and the Algerian dialect, demonstrating its effectiveness in addressing restaurant food safety concerns
Torque ripple minimization and performance enhancement of switched reluctance motor for electric vehicle application
Get PDFSwitched reluctance motors (SRMs) are an attractive choice for electric vehicle (EV) applications but suffer from certain limitations, such as high torque ripple and acoustic noise. This paper presents ongoing research and development activity details to enhance the performance of SRMs for EV applications. The poor performance of a conventional SRM which is available in market with a rating of 8/6 poles, 48 V, 500 W, and 2,000 rpm is tested. A motor model of the same rating is developed using ANSYS Maxwell software. Motor performance parameters important for EV applications, such as efficiency, rated torque and torque ripple are compared with the conventional motor. One novel technique to reduce the torque ripple of SRM is discussed along with the results. Torque ripple of developed software model is reduced by 24.52% without a reduction in the efficiency and rated torque of the motor. The performance of the developed SRM software model is better compared to conventional SRMs available in the market. 2D and 3D models of SRM were presented using ANSYS Maxwell software
Smart enterprise architecture framework for developing patent office
Get PDFTechnology and communication’s impact on daily life makes innovation vital for economic growth, highlighting prizing intellectual property (IP) asset protection and management. Patent office, pivotal custodians of legal frameworks and repositories of IP assets, grapples with significant challenges, and backlogs stemming from escalating patent applications and outdated processes. Patent office encounters the challenge of balancing innovation and IP protection because of the convergence of rapid advancements in technologies, for instance, AI, and blockchain. This research employs a design science research methodology to generate a tailored framework addressing these multifaceted challenges. The proposed smart enterprise architecture (SEA) framework offers a strategic, multidimensional approach to modernizing the patent office. It integrates principles from enterprise architecture, information systems management, and IP law, emphasizing efficiency, scalability, and security. The framework leverages the quadruple helix model, fostering collaboration between government, industry, academia, and civil society to enhance stakeholder engagement and innovation ecosystems. Optimizing patent office functions and adapting to IP management’s evolution, the SEA framework integrates technology and organizational goals for a comprehensive approach
Implementation of an app-controlled robotic arm to optimize loading processes in Callao-Peru
Get PDFEfficient handling of heavy loads represents a constant challenge for businesses, which traditionally rely on significant numbers of staff, involving considerable financial costs and occupational health risks exacerbated by the need for specialized infrastructure. Despite technological limitations and structural deficiencies, this solution has prevailed in practice. However, engineering has responded with innovations aimed at optimizing these processes. In this context, the study proposes to adopt an approach based on implementing a robotic arm supported by technologies such as Arduino, Bluetooth devices, servo motors, and remote-control software developed in App Inventor. This approach promises not only the reduction of labor costs and the improvement of job security but also a positive impact in social and economic terms. A preliminary prototype is presented that validates the basic functionality of the proposed robotic arm. This study presents a technically and economically viable alternative for managing heavy loads in enterprise environments, reducing dependence on a large workforce, and improving operational efficiency
Intelligent voice control system for UAV with mobile robot
Get PDFThe article presents a voice control system for unmanned aerial vehicles (UAVs) and an integrated mobile robot, based on artificial intelligence (AI). The system recognizes voice commands in the Kazakh language, converted into Latin transliteration, providing intuitive control of the UAV and robot. The performance of the system in various scenarios including agriculture, environmental monitoring and search and rescue operations is investigated. The system showed high accuracy of command recognition (95%) and efficient control of the UAV and robot. The proposed system opens up new possibilities for the use of UAVs and robots in various fields, increasing their autonomy, flexibility and ease of use
IoT-based real-time monitoring of river water quality: a case study of the Selangor River
Get PDFMonitoring river water quality is crucial for preserving freshwater ecosystems, ensuring public health, and supporting resource management. Traditional methods, while accurate, lack the scalability and real-time capabilities needed for proactive intervention. This study introduces an IoT based water quality monitoring system for the Selangor River, integrating sensors for pH, temperature, turbidity, and total dissolved solids (TDS) with a NodeMCU ESP32 microcontroller. To complement the IoT system, a handheld test pen was used to measure salinity and electrical conductivity (EC), offering additional insights into water quality. Field tests at four stations along the river revealed significant spatial variations. Station 1, near the river mouth, showed high salinity, EC, and TDS, indicating saltwater intrusion, with relatively low turbidity. Stations 2 and 3 recorded the highest turbidity levels, suggesting sedimentation and upstream activities, with moderate salinity and EC. Station 4, upstream, demonstrated stable freshwater characteristics, with low salinity, EC, and turbidity levels. The IoT system reliably monitored real-time parameters, and its measurements were validated against those from the handheld test pen. Minor discrepancies in TDS and temperature readings highlighted the importance of calibration
The development of contextual chat interactions with retrieval-augmented generation system for facilitating learning hadith
Get PDFThis study explores the development and implementation of a retrieval-augmented generation (RAG) system using the large language model (LLM) to enhance the learning of hadith through a chat interface for high school students. This study addresses challenges in optimizing RAG configurations and problems associated with traditional educational methods that lack interactivity. In addition, the RAG system was designed to replace real teacher interactions, offering a chat feature that provides contextual answers to real-life scenarios related to Hadith. Various configurations were tested, with a focus on the Matn component, achieving a high accuracy score with a mean of .754 and demonstrating efficiency in context relevance with a mean of .797. Results indicated significant accessibility using our RAG system for learning hadith via WhatsApp’s chat interface. Hence, this study highlights the potential of RAG systems in transforming educational environments and offers insights into the development of technology for interactive Hadith learning solutions