Proposed System Research Articles

Improvement of Non-Business Development (NBD) Investment Proposal System Through a Pre-Proposal System at PT Pertamina Hulu Rokan

PT Pertamina Hulu Rokan has experienced a decline in net profit, while new work proposals and carry-over (CO) projects have continued to rise. This trend raises concerns about resource management and operational efficiency, requiring an effective solution. This research aims to develop a pre-proposal system using risk-based and workload analysis to prioritize and evaluate Non-Business Development (NBD) investment proposals. The system ensures alignment with strategic objectives and resource capacity. The study introduces a structured pre-proposal process to filter and prioritize proposals at an early stage. The system focuses on critical and feasible projects by applying risk and workload evaluations while reducing redundancies. The research aims to optimize decision-making and align investment proposals with corporate strategies. The methodology includes Business Process Modeling Notation (BPMN) simulations to analyze workflow inefficiencies and Focus Group Discussions (FGD) with stakeholders to refine the system. A trial of the pre-proposal tools for the 2026 Work Plan and Budget (RKAP) demonstrated their effectiveness in managing and prioritizing proposals. The results show that the pre-proposal system reduces incoming proposals from 299 work plans to 232 work plans, a 22.4% decrease. Proposals are categorized into P1 (very high priority) and P2 (high priority) for follow-up, ensuring resources are directed toward impactful projects. This research provides a practical framework for early-stage proposal assessment, enhancing decision-making and investment planning. The system demonstrates the potential to improve efficiency, reduce redundancies, and better align proposals with organizational goals, offering a valuable tool for managing future investment initiatives.

Design of an LED and Laser Diode Based Optical Vehicle Alerting System

The quiet operation of electric and hybrid vehicles at low speeds can pose a risk to pedestrians. To enhance pedestrian safety, Acoustic Vehicle Alerting Systems (AVAS) have been developed. The objective of this study is to develop an Optical Vehicle Alerting System (OVAS) that is integrated with vehicle speed data, with the aim of improving pedestrian awareness. By processing speed data transmitted via the Controller Area Network (CAN) bus, a variety of light patterns were projected on the road surface using optical sources in daylight conditions. In the pro-posed system, the efficacy of linear laser-based warning patterns generated by laser sources was compared with that of circular light patterns produced by arrays of light-emitting diodes (LEDs) com-bined with lens structures. A vehicle simulation was conducted to measure the light intensity and illumination profiles of laser and LED-based systems in a test environment. The findings revealed that an alerting pattern was generated with an illuminance of 90 lux at a distance of five metres when six LEDs with a wavelength of approximately 505 nm and an output power of 5 mW were employed. Similarly, a dynamic, speed-dependent linear alerting pattern was generated using three laser diodes operating at a wavelength of 532 nm and an output power of approximately 100 mW. The effectiveness of light intensity and illumination profiles was evaluated based on performance at different vehicle speeds. The OVAS system designed with LEDs was mounted at two different heights, 30 cm and 50 cm above the ground, and their performances were compared. It was observed that the OVAS positioned at 50 cm projected a light pattern covering 242% more area at a range of 1-3 metres compared to the system mounted at 30 cm. This emphasises the considerable impact of the installation height on the system's efficacy.

A Proposed Agent System for Network Monitoring

The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presented in real time which is minimizing the cost that represents the important factor to successful management of networks that was achieved using agents.

OPTIMIZATION AND SIZING OF AN ISLAND MICROGRID BASED ON PHOTOVOLTAIC/FUEL CELL (PV/FC) IN KSA

The major goal of this research manuscript is to conduct economical and feasibility studies of an island photovoltaic/fuel cell (PV/FC) hybrid system. It has been designed to feed electricity to a remote area positioned in albaha zone in Kingdom of Saudi Arabia. To achieve this mission, all system devices are modelled and simulated via optimization mechanisms. An objective function is formulated relied on the total annual cost (TAC). An optimization mechanism, the Flower Pollination Algorithm (FPA) has been applied to obtain the optimum number of the photovoltaic (PV) panels, the electrolyzers; the H2 storage tanks the fuel cells (FCs) and the number of batteries for the proposed system with the least total net present value (TNPV) of the hybrid system. The loss of power supply probability (LPSP) has been utilized to improve the performance of the proposed system. The premier estimation fluctuation effect of the FC, the electrolyzer and the H2 storage tanks on the levelized cost of energy (LCOE) has been applied in the sensitivity test.

Knowledge Representation of Sensor Dataset with IoT Collaboration of Semantic Web and IoT: Storage of Temperature and Humidity Details

Introduction: Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data. Method: Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries. Result: This approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis. Conclusion: As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Analysis and Design of CUK-SEPIC-based Converter for Hybrid Power Generation Systems

Background: The increasing demand for electricity, coupled with an imbalanced supply and demand, population growth, and climate change, has prompted the shift from conventional to non-conventional energy systems. However, the unreliability and intermittency of the latter pose a challenge to their feasibility. To address this challenge, a proposal has been made to explore the combination of two renewable energy sources (RES) using a unique DC-DC converter topology, with the aim of meeting the load demand in a sustainable and efficient manner. Objective: The focus of this research was to explore solutions for the challenges associated with operating RES independently, including issues with intermittency, weather dependence, and meeting load demands. The proposed hybrid system features exclusively RES, offering a promising approach to reducing carbon footprint. Ultimately, we aimed to develop a CUK-SEPIC-based converter that can effectively integrate two independent RES to satisfy the load demand of a standalone application. Method: Effective hybrid power generation through RES is a complex challenge, but it has been found that combining solar and biomass energy sources is one of the best options for achieving this goal. To tap into these sources, it is essential to have a suitable power electronic converter, and the CUK-SEPIC converter has been chosen for its many benefits. The features of this converter have been described in detail. The integration of solar and biomass energy sources is achieved using this converter, which has been designed and mathematically modeled in the MATLAB/Simulink environment to ensure optimal performance. To validate the effectiveness of the proposed converter, a comparison with existing power electronic converters has been done using the MATLAB/Simulink platform. Results: The hybrid power generation system model has been comprehensively developed in this work using the sophisticated MATLAB/Simulink environment. The input and output parameters have been diligently estimated through an extensive simulation process. The research has yielded valuable insights, indicating that the CUK-SEPIC converter exhibits an impressive power conversion efficiency of 96.57%, along with an overall step-up ratio of 5.25 and significantly reduced ripple content. Conclusion: Upon conducting a comprehensive analysis of the CUK-SEPIC DC-DC converter, it has been observed that the proposed system exhibits significant promise in rectifying the reliability issues commonly associated with renewable energy power generation. Therefore, it is recommended that this system be considered for implementation in rural electrification initiatives. Furthermore, it is worth noting that this system represents one of the most recent developments in the field of renewable energy power generation technology.

Harnessing Intelligent RIS for Optimized Capacity and Latency in 6G Cooperative NOMA Systems

This article examines the performance of cooperative NOMA systems in a massive MIMO configuration inside the sixth-generation (6G) network, including scenarios with and without reconfigurable intelligent surfaces (RIS). The emphasis is on comprehending the effects of implementing static, dynamic, and intelligent RIS on the capacity of the cooperative NOMA system with differing user quantities. The study examines how different user loads and RIS densities affect average latency and effective area spectral efficiency (EASE). Integration of a proposal system in a novel way with water-filling in the multi-user channel logarithm to enhance capacity, average latency, and EASE for diverse user densities. The results show that RIS significantly improves capacity, average latency, and EASE, especially with intelligent RIS. All scenarios, in conjunction with the suggested algorithm, markedly improve network performance, particularly under conditions of increased user demand. Additionally, it enhances the system's capability and spectrum efficiency, especially when deployment numbers rise.

DESENVOLVIMENTO DE MATRIZ DE SÍLICA AMINOFUNCIONALIZADA COMO POTENCIAL ADSORVENTE DE ÍONS Pt4+ EM SISTEMAS DE PRÉ-CONCENTRAÇÃO PARA ANÁLISE POR FAAS

DEVELOPMENT OF AMINOFUNCTIONALIZED SILICA MATRIX AS A POTENTIAL ADSORBENT FOR Pt4+ IONS IN PRECONCENTRATION SYSTEMS FOR FAAS ANALYSIS. The use of materials such as organofunctionalized silica for application in solid phase extraction (SPE) allows the preconcentration of several ions, including Pt, and subsequent detection. This work aims to synthesize and characterize an organofunctionalized silica (Sil-TMSDT) with the silylant N’(trimethoxysilyl)propylethylenetriamine and its application as a solid phase in the preconcentration Pt4+ ions in aqueous media, using detection by flame atomization atomic absorption spectroscopy (FAAS). The Fourier-transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) coupled with energy-dispersive spectroscopy (EDS) confirmed the aminofunctionalization of the silica. For the adsorption process, a preconcentration mini-column with 200 mg of Sil-TMSDT, a solution of 0.485 mg L-1 of K2[PtCl6] at pH 10 and HNO3 0.5 mol L-1 as eluent were used, obtaining an enrichment factor of 15-fold with retention efficiency higher than 99%. The Pt4+ ion determined by FAAS showed a linear response between 0.02 and 0.40 mg L-1; a linear coefficient where R = 0.9981; a 16-fold preconcentration factor; a limit of quantification (LOQ) and limit of detection (LOD) of 0.045 and 0.014 mg L-1, respectively. The proposal system showed analytical applicability to determining Pt4+ ion in an aqueous medium and could be used to detect traces of this potentially toxic metal in biological fluids such as urine and blood.

CYBERSYMBIOSIS OF HUMAN JUDGES AND ARTIFICIAL INTELLIGENCE: PROBLEMS AND POTENTIAL SOLUTIONS FOR INTEGRATION AND FOR THE SUCCESSFUL MODERNIZATION OF THE JUDICIAL SYSTEMS OF THE BRICS COUNTRIES

Abstract. This article examines the situation of cybersymbiosis between humans and artificial intelligence in order to consider how this phenomenon can be applied in the BRICS countries. The article proposes a structure that will allow the use of expert systems and the capabilities of people in order to improve justice with the integrated use of modern technologies. Using the research design discussed in the article, it can be noted that artificial intelligence is expected to help cope with tasks in the analysis and systematization of various phenomena, and intelligent models make it possible to create a system of responsibilities and proposals that can take a personalized approach to the issue of rulemaking. The main goal of this article is thoughtful cooperation within the framework of cybersymbiosis between humans and artificial intelligence, which will help the BRICS countries to target the vector of development of law-making activities, including such provisions of the vector of change as court cases, cases, writing regulations and much more. However, there remain a number of threats that need to be resolved in order to get rid of ethical and other misunderstandings on the part of artificial intelligence developers, the operation of various automated systems and other problems. In addition, the article offers a number of suggestions and solutions that will help potentially get rid of the above problems.

Application of Interactive Evolutionary Computing in Personalized Interior Design

Design is a complex and clever process with multiple concepts attempting to explain it. To validate ideas and quantitatively characterize the design process, design experiments, and data analysis are required and predictable. Applying data analysis in design experiments can be challenging, and data are often underutilized. In this study, we develop an experimentation Interactive Evolutionary Computing Design (IECD) model using Upgraded Particle Swarm Optimization (UPSO) within the personalized interior design to iteratively analyze the new color combinations based on 116 designer’s preferences. The study proposed data-infused research into interior design via experimental techniques and the proposal system is evaluated, with additional feedback collected from experimented 116 designers, and data visualization analysis using Color Histogram Analysis and Auto encoders for data’s structure and patterns of the data, thus enhancing user preferences. A proposal system is developed using the collected data to suggest design options that align with individual user preferences and then integrated into the IECD framework to generate personalized design solutions that are more efficient and intelligent. The results are the effectiveness of data analysis and visualization in revealing design patterns and the impact of the data-infused IECD system to enhance the personalization and efficiency of interior design solutions. The IECD system enhances interior design efficiency and personalization through a proposal system, revealing design patterns, and aligning options with individual user preferences.

Legal Imperatives for the Implementation of Energy Auctions in Nigeria

AbstractWith the passage of the Climate Change Act, and to help meet its net zero obligations by 2060, Nigeria must transition from its dependence on fossil fuel energy sources to renewable energy. This will involve the procurement of large amounts of renewable energy by the government. In the past, procurement of power from the government-owned bulk trader has been chaotic, with no discernible strategy, and it is doubtful whether the government or Nigeria's citizens have derived value for money from the process. This article suggests a transition from the current, mostly unsolicited, proposal system to energy auctions, as the authors believe that this will help the country achieve low prices for renewable energy. The article also examines polices that have been implemented in other countries to drive energy auctions, with a view to applying relatable practices to the Nigerian exercise.

PASS POINT SELECTION OF AUTOMATIC GRAPHICAL PASSWORD AUTHENTICATION TECHNIQUE BASED ON HISTOGRAM METHOD

Graphical passwords, as opposed to textual passwords, require the user to pick pictures or draw symbols rather than input written letters. They are an option that may be explored in order to get over the issues that are caused by the system of passwords that are based on text. It has been hypothesized that graphical passwords are more difficult to crack using a brute force technique or to figure out through guessing. This paper proposes an authentication system based on a graphical password method. The proposed system computes the password points using histogram arithmetic and encrypts the chosen password points using SHA512. The envisioned system has been realized as an android application and evaluated with existing research considering multiple measurements such as required login time, password space, and entropy. The findings reveal that the new suggested system outperforms the reference work by more than 85% in terms of login latency and more than 72% in terms of entropy results.

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.

A textual question answering and handwritten answer evaluation system for hindi language

Textual Question Answering targets answering questions defined in natural language. Question Answering Systems offer an automated approach to procuring answers to queries expressed in natural language. The need for Multilingual Question Answering without performing machine translation is ever existing. Besides that, automating tasks with the help of technology to assist humans, has been the main aim of research in recent years. This paper presents an automated answer evaluation system for reading comprehension-based questions in the Hindi language without requiring translation in any other language. The system accepts text, question, and handwritten answer of a student in the form of an image for answer evaluation. This is accomplished by developing a textual question-answering system for reading comprehension. It is an extractive approach that utilizes RoBERTa transformer model and fine-tunes it for Hindi question-answering. The answer to the question is extracted as a span from the provided text. Further, a handwritten text recognizer model is developed employing a Convolutional Recurrent Neural Network with Connectionist Temporal Classification module along with two layers of Bidirectional LSTM. Experimentation is performed using existing as well as self-created datasets to show the effectiveness of the proposed approach. An accuracy of 98.69% is obtained on the self-created Hindi-QA dataset and the proposed system outperformed the other existing methods. The paper also discusses potential research directions in the field.

VisionCoach: Design and Effectiveness Study on VR Vision Training for Basketball Passing.

Vision Training is important for basketball players to effectively search for teammates who has wide-open opportunities to shoot, observe the defenders around the wide-open teammates and quickly choose a proper way to pass the ball to the most suitable one. We develop an immersive virtual reality (VR) system called VisionCoach to simulate the player's viewing perspective and generate three designed systematic vision training tasks to benefit the cultivating procedure. By recording the player's eye gazing and dribbling video sequence, the proposed system can analyze the vision-related behavior to understand the training effectiveness. To demonstrate the proposed VR training system can facilitate the cultivation of vision ability, we recruited 14 experienced players to participate in a 6-week between-subject study, and conducted a study by comparing the most frequently used 2D vision training method called Vision Performance Enhancement (VPE) program with the proposed system. Qualitative experiences and quantitative training results are reported to show that the proposed immersive VR training system can effectively improve player's vision ability in terms of gaze behavior and dribbling stability. Furthermore, training in the VR-VisionCoach Condition can transfer the learned abilities to real scenario more easily than training in the 2D-VPE Condition.

Development of a method for refining recommendations using temporal knowledge in individual insurance tasks

The subject of the research is the process of constructing recommendations in individual insurance tasks. The aim is to develop an approach for building recommendations in personalized insurance projects using temporal knowledge to adapt proposed insurance plans according to client behavior on insurance company websites. Objectives: to structure temporal rules considering the peculiarities of the online insurance recommendation process; to develop a method for constructing recommendations for insurance product selection using temporal knowledge. Conclusions: temporal rules have been structured for the task of building recommendations in personalized insurance projects. These rules generalize behavior in relative time for multiple users, enabling the generalization of user action sequences. A method for constructing online recommendations using temporal knowledge has been developed. The method consists of two phases: temporal knowledge formation and recommendation construction. The first phase is performed offline and is designed to form a base of temporal rules that generalize knowledge about user behavior. The second phase is executed online and involves refining and further utilizing temporal rules to adapt recommendations according to current user behavior. The method enables prompt adaptation of recommendations considering the user's current actions, creating conditions for increasing user trust in the recommender system's proposals.

Model of the “Economic Cross” of the Digital Platform: Opportunities and Conditions for Interindustry Technological Convergence

The platform economy is an objective stage in the evolution of the digital economy, whose development is carried out under the influence of the trend of involving a large number of SMEs and individuals in economic relations as their direct participants. Unlike the pre-digital era, when operational commercial direct interaction between small participants in economic relations turned out to be impossible, communication technologies of the second decade of the 21st century have created favorable opportunities for the development of independent businesses of individuals and small professional teams. The process of commercialization of household resources is actively taking place in the form of the development of private blogs as platforms for commercial advertising and mutual trade of goods and services by individuals online, which creates a direct competitive threat to traditional trading companies. The current stage in the development of online participation of SMEs and individuals in commercial relations is the development of outsourcing relationships, within which part of the processes implemented by large businesses independently using hired personnel and their own infrastructure will be delegated to independent network structures on a competitive basis. At the same time, in proportion to the growing popularity of e-commerce, public demands for the security of such transactions are increasing, which is especially important in conditions of sanctions uncertainty and high risks of supply chain collapse. This circumstance determines the business request for systematization of the digital space and its centralization, the terminal stage of which is an inter-industry digital platform. The purpose of the scientific article is to develop a system of proposals for the formation of a national intersectoral outsourcing platform based on the “economic cross” model. A system of innovative solutions has been developed regarding the organization of economic interaction between subjects, the procedure for financing the activities of the platform by its participants, and the distribution of indirect industry income arising from the functioning of the platform.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Automated Disaster Monitoring From Social Media Posts Using AI-Based Location Intelligence and Sentiment Analysis

Worldwide disasters like bushfires, earthquakes, floods, cyclones, and heatwaves have affected the lives of social media users in an unprecedented manner. They are constantly posting their level of negativity over the disaster situations at their location of interest. Understanding location-oriented sentiments about disaster situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI) and natural language processing (NLP), for extraction of location-oriented public sentiments on global disaster situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to disaster in 110 languages through AI- and NLP-based sentiment analysis, named entity recognition (NER), anomaly detection, regression, and Getis Ord Gi* algorithms. We deployed and tested this algorithm on live Twitter feeds from 28 September to 6 October 2021. Tweets with 67 515 entities in 39 different languages were processed during this period. Our novel algorithm extracted 9727 location entities with greater than 70% confidence from live Twitter feed and displayed the locations of possible disasters with disaster intelligence. The rates of average precision, recall, and F₁-Score were measured to be 0.93, 0.88, and 0.90, respectively. Overall, the fully automated disaster monitoring solution demonstrated 97% accuracy. To the best of our knowledge, this study is the first to report location intelligence with NER, sentiment analysis, regression and anomaly detection on social media messages related to disasters and has covered the largest set of languages.