International Journal of Informatics and Communication Technology (IJ-ICT)
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Self-adaptive firefly algorithm-based capacitor banks and distributed generation allocation in hybrid networks
Get PDFPower system deregulation has made significant changes to the power grid through various technologies, privatization of entities, and improved efficiency and reliability. This work mainly focuses on different combinations of distributed generation (DG) and capacitor banks (CBs) integration to cater to multiple technical, economic, environmental, and reliable concerns. A new optimal planning framework is proposed for optimally allocating the DG units and CBs to achieve multiple objectives. In this work, an augmented objective function is formulated by considering active power losses, voltage deviation, and voltage stability index objectives. This objective function is solved considering various equality and inequality constraints. This work proposes a novel approach for allocation of DGs and CBs in the radial distribution systems (RDSs) using an evolutionary-based self-adaptive firefly algorithm (SAFA). The effectiveness of the developed planning approach is demonstrated on IEEE 33 bus RDS in MATLAB software. The obtained results indicate that proposed planning approach resulted in reduced power losses, voltage deviations, and improved voltage stability
Enhancing intellectual property rights management through blockchain integration
Get PDFThe generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device
Lightweight deep learning approach for retinal OCT image classification: A CNN with hybrid pooling and optimized learning
Get PDFOptical coherence tomography (OCT) is a non-invasive technique through which a retina specialist can see the structure behind the eye. This technol ogy offers a key role to identify various abnormalities in the retina: Drusen, diabetic macular edema (DME) and choroidal neovascularization (CNV). However, manual analysis of OCT scans can be time-consuming and prone to variability among clinicians. To address this challenge, we present a lightweight and explainable deep learning-based approach for automatic classification of retinal OCT images. The primary goal of this research is a model that delivers high diagnostic accuracy. A computer-aided suggestive method can help retinal doctors automatically classify the anomalies with more confidence and precision. In this paper, we proposed a novel approach based on deep learning: a six-layer convolutional neural network (CNN) integrated with hybrid pooling for effective feature extraction. Data augmentation and exponential learning rate is implemented to handle data imbalance between classes and for stabilized learning consecutively. Our proposed approach achieved 98.75% of accuracy while testing on the dataset. To further enhance the interpretability of the model, we also integrate explainable AI (XAI) using class activation mapping (CAM) to visualize the critical regions in the retina that contribute to the classification decisions
Analysis of congestion management using generation rescheduling with augmented Mountain Gazelle optimizer
Get PDFThis study presents an original blockage of the executive’s approach utilizing age rescheduling with the augmented mountain gazelle optimizer (AMGO). Enlivened by the versatility of mountain gazelles, AMGO is applied to enhance age plans for a reasonable power framework situation. The strategy successfully mitigates clogs, taking into account functional imperatives, market elements, and vulnerabilities. Recreation results show AMGO’s heartiness, seriousness, and proficiency in contrast with existing strategies. Notwithstanding its heartiness in blockage the board, the AMGO presents a state-of-the-art versatile element, enlivened by the spryness of mountain gazelles, empowering constant changes in accordance with developing power framework conditions and contrasted and genetic algorithms and PSO. The review adds to propelling streamlining methods for clogging the executives, offering a promising device for improving power framework, unwavering quality and productivity
Efficient blockchain based solution for secure medical record management
Get PDFElectronic medical records (EMRs) have become a key player in the healthcare ecosystem contributing to the assessment of ailments, the choice of the treatment avenue, and the delivery of services. However, there is consideration of EMR storage whereby centralized storage leads to increased security and privacy issues in the patient’s record. In this paper, we proposed a blockchain and interplanetary file system (IPFS) based prototype model for EMR management. It provides a smart contract-enabled decentralized storage platform where healthcare data security, availability, and access management are prioritized. This model also employs cryptographic techniques to protect sensitive healthcare data. Finally, the model is evaluated in a realistic scenario. The experimental results demonstrate that compared to the current systems, the proposed prototype model outperforms them in terms of efficiency, privacy, and security
Prediction of international rice production using long short-term memory and machine learning models
Get PDFRice, a staple food source globally, is in high demand and production across the world. Its consumption varies in different countries, with each nation having its unique way of incorporating rice into its diet. Recognizing the global nature of rice, its production is a crucial aspect of ensuring its availability, agriculture forecasting, economic stability, and food security. By predicting its production, we can develop a global plan for its production and stock, thereby preventing issues like famine. This paper proposes machine learning (ML) and deep learning (DL) models like linear regression, ridge regression, random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting, decision tree, and long short-term memory (LSTM) to predict international rice production. A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R2 ), 98.40% and 98.19%, respectively. These predictions and the developed models can play a vital role in resolving crop-related international problems, uniting the global agricultural community in a common cause
Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer
Get PDFIn this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one
A hybrid machine learning approach for improved ponzi scheme detection using advanced feature engineering
Get PDFPonzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods
The 360° beach video: a supporting mindfulness intervention with virtual reality
Get PDFThis article describes optimizing virtual reality (VR) with a 360° beach video model used for mindfulness interventions. Using VR with 360° beach videos to support the presence of an immersive environment can effectively support mindfulness practices. Students are interested in the integration of technology in school counseling. VR helps in creating immersive environments such as forests, beaches, waterfalls, etc. so that students focus more on practicing mindfulness and attention in the current moment. This article focuses on optimizing 360° beach videos in the breathing mindfulness process so that it helps bring out real experiences. Obstacles to practicing mindfulness include lack of focus, mind wandering and not concentrating. through the use of 360° beach videos with VR can increase focus and be more effective in mindfulness practice
Interoperability in healthcare: a critical review of ontology approaches and tools for building prescription frameworks
Get PDFEfficient healthcare interoperability is pivotal for delivering high-quality patient care. This research article presents a critical review of ontology based approaches and tools in the development of ontology-based electronic prescriptions (e-prescription), with a focus on enhancing healthcare interoperability. The investigation encompasses two major domains: ontology overview and healthcare interoperability using semantic e-prescription. In the ontology overview, we scrutinize various aspects of ontology development, including the methodologies, languages, tools, and evaluation metrics adopted from literature. Notable comparisons between ontologies and databases are explored. Additionally, we delve into the challenges associated with ontology development and provide a comprehensive summary of methodologies, languages, tools, and evaluation approaches. Healthcare interoperability using semantic e-prescription undertakes a detailed review of e-prescription systems, emphasizing their critical role in healthcare interoperability. A thorough examination of frameworks facilitating semantic e-prescription is presented, offering a nuanced perspective on their contributions and limitations. The section concludes with a concise summary of the key findings from the e-prescription framework review. The article further addresses challenges in healthcare interoperability, including data standardization and system integration issues. To direct continuing research efforts that integrate cutting-edge technologies and interdisciplinary collaborations, future directions and emerging trends are outlined