bluuebunny/crossref_metadata_2025 huggingface dataset:
The feasibility of processing waste from religious ceremonies in Bali as clean energy
Authors: Nugraha I Made Aditya , Desnanjaya I Gusti Made Ngurah
Published: August 2024

Bali is one of the islands with the largest Hindu religion in Indonesia. This is of course an attraction for tourists to see the culture and natural beauty that the island of Bali has to offer. However, apart from that, there are many religious activities that occur on the island of Bali and the waste produced is something that needs special attention. If this waste is not handled properly, it will threaten environmental sustainability in Bali and indirectly the tourism offered. Therefore, there is a need for a solution to overcome the waste problem. By conducting observations, interviews and literature studies, a way to overcome this problem was found, namely by converting the waste into aromatherapy incense, vermicomposting, briquettes and biofuel. The results of this processing have been studied and of course have the potential to be carried out on the island of Bali. The application of this method also indirectly plays an important role in preserving the environment and economy on the island of Bali.
13-level modular multilevel inverter application for the exhaust fan drive control of Thu Thiem Road tunnel
Authors: Anh An Thi Hoai Thu , Cuong Tran Hung
Published: August 2024

The ventilation system plays a vital role in ensuring the safety of people and means of transportation. Fresh air is created in the tunnel mainly thanks to the exhaust fans arranged at the top of the tunnels. The drive motor for the exhaust fan in the Thu Thiem Road tunnel has a power of 560 kW and operates at a voltage of 6 kV. The paper proposes a 13-level modular multilevel inverter (MMC) with the improved nearest level modulation (NLM) method to ensure the quality of voltage output from the voltage source inverter-fed exhaust fan drive motor. This is a novel combination aimed at transforming electrical power at high voltage levels, high power, and enhancing operational efficiency and the lifespan of semiconductor components within the inverter when operating continuously and over extended durations. The theoretical research results verified through MATLAB/Simulink software with simulation parameters collected from the exhaust fan motor of Thu Thiem Road tunnel, Vietnam show total harmonic distortion of the current in operation with 13 levels is 1.23%, while that of the current in operation with 7 levels is 10.1%; total harmonic distortion (THD) of the voltage with 13 levels is 5.33%, while that of the voltage with 7 levels is 11.37%.
Solar-based aerator with water quality monitoring in vannamei shrimp pond
Authors: Pratama I Putu Eka Widya , Kusuma Friska Aprilia , Mujiyanti Safira Firdaus , Schirhagl Romana , Nanta Tepy Lindia
Published: August 2024

The water quality is vital for the vannamei shrimp pond's productivity. Manual monitoring at Gunung Anyar's vannamei shrimp pond is time-consuming, ineffective, and potentially harmful. In this research, we developed a real-time monitoring system for the water quality of the vannamei shrimp pond. This monitoring system is integrated with a solar-based aerator. To address this, water quality monitoring in a solar-based aerator system tracks the degree of acidity (pH), temperature, and total dissolved solids (TDS) remotely using a website and real-time mobile phone Android application with 98.57% accuracy and 1.43% error. Seven days of data revealed the degree of acidity between 6.92 and 7.34 is indicated poor conditions of the pond While the temperatures from 23.59 °C to 38.32 °C, and TDS from 628.65 to 652.34 ppm indicate the good condition of the shrimp pond. This real-time monitoring system can help vannamei shrimp farmers monitor the actual conditions of their ponds.
Phase structures-based hybrid approaches for defect detection in vials
Authors: Vishwanatha C. R. , Asha V.
Published: August 2024

Quality control and assurance in pharmaceutical vial manufacturing are paramount to ensure drug safety and efficacy. Defects such as cracks, bubbles, black spots, and wrinkles can compromise product quality and patient safety. This study proposes a novel methodology that integrates fast non-local means (FNLM) filtering with hybrid image processing techniques to detect these defects. Previous approaches have often struggled with subtle anomalies in texture and surface features. The proposed solution leverages phase structure analysis, utilizing phase stretch transform (PST) to effectively highlight subtle anomalies by extracting features sensitive to phase variations. These features are further refined using Gaussian filtering, with Otsu thresholding applied for precise segmentation and defect boundary identification. Morphological dilation enhances detection speed and accuracy, while region of interest (ROI) identification aids in localizing defects and facilitating decision-making. The system demonstrates significant improvements in quality control, achieving high performance metrics: precision (98.85%), recall (98.57%), accuracy (98.36%), specificity (98.0%), and F1-score (98.71%). It also achieves impressive AUC-ROC (98.18%) and AUC-PR (99.08%) values, demonstrating its robustness and suitability for defect detection in pharmaceutical vials.
Deep reinforcement learning based quality of experience aware for multimedia video streaming
Authors: Reddy Manjunatha Peddareddygari Bayya , Narayanappa Sheshappa Shagathur
Published: August 2024

Video streaming involves the continuous delivery of video files from a server to a client, where multimedia streaming is employed for playback through an online or offline media player. Video streaming uses live broadcasts to enhance direct communication with community partners and customers. The existing methods have less video streaming quality and are unable to efficiently adapt to the dynamic conditions of the network. In this research, an adaptive bit rate (ABR) method depending on dynamic adaptive video streaming over hypertext transfer protocol or HTTP (DASH) based deep reinforcement learning (DRL) named DASH-based DRL is proposed to determine the following segment’s quality in DASH video streaming with wireless networks. The proposed algorithm significantly improves the quality of experience (QoE) performance by providing a highly stable video quality, reducing the distance factor, and enduring smooth streaming sessions. The performance of the proposed method is analyzed based on performance measures of performance improvement, QoE metrics, mean opinion score, normalized value of QoE, average of normalized value of QoE, switching quality, and rebuffering time. The suggested algorithm obtains a high average normalized QoE of 0.72, average switching quality of 0.15, and an average rebuffering time of 0.16 sec, which is comparatively superior to other algorithms like real-time streaming protocol (RTSP), HTTP live streaming (HLS) and reinforcement learning (RL).
Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern
Authors: Riaz Imran , Ali Ahmad Nazri , Ibrahim Haidi , Huqqani Ilyas Ahmad
Published: August 2024

In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance.
New image encryption approach using a dynamic-chaotic variant of Hill cipher in Z/4096Z
Authors: Rrghout Hicham , Kattass Mourad , Qobbi Younes , Benazzi Naima , JarJar Abdellatif , Benazzi Abdelhamid
Published: August 2024

Currently, digital communication generates a considerable amount of data from digital images. Preserving the confidentiality of these images during transmission through network channels is of crucial importance. To ensure the security of this data, this article proposes an image encryption approach based on enhancing the Hill cipher by constructing pseudo-random matrices operating in the ring Z/212Z injected into a controlled affine transformation. This approach relies on the use of chaotic maps for generating matrices used in the encryption process. The use of the ring Z/212Z aims to expand the key space of our cryptosystem, thus providing increased protection against brute-force attacks. Moreover, to enhance security against differential attacks, a matrix of size (4×4), not necessarily invertible, is also integrated into a diffusion phase. The effectiveness of our technique is evaluated through specific tests, such as key space analysis, histogram analysis, entropy calculation, NPCR and UACI values, correlation analysis, as well as avalanche effect assessment.
Relationship between features volatility and bug occurrence rate to support software evolution
Authors: Hadiningrum Tiara Rahmania , Mardiana Bella Dwi , Rochimah Siti
Published: August 2024

Software evolution is an essential foundation in delivering technology that adapts to user needs and industry dynamics. In an era of rapid technological development, software evolution is not just a necessity, but a must to ensure long-term relevance. Developers are faced with major challenges in maintaining and improving software quality over time. This research aims to investigate the correlation between feature volatility and bug occurrence rate in software evolution, to understand the impact of dynamic feature changes on software quality and development process. The research method uses commit analysis on the dataset as a marker of bug presence, studying the complex relationship between feature volatility and bug occurrence rate to reveal the interplay in software development. Validated datasets are measured by metrics and correlations are measured by Pearson-product-moment analysis. This research found a strong relationship between feature volatility and bug occurrence rate, suggesting that an increase in feature changes correlates with an increase in bugs that impact software stability and quality. This research provides important insights into the correlation between feature volatility and bug occurrence rates, guiding developers and quality practitioners to develop more effective testing strategies in dynamic development environments.
Deep neural networks for removing clouds and nebulae from satellite images
Authors: Glazyrina Natalya , Muratkhan Raikhan , Eslyamov Serik , Murzabekova Gulden , Aziyeva Nurgul , Rysbekkyzy Bakhytgul , Orynbayeva Ainur , Baktiyarova Nazira
Published: August 2024

This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging spatial attention mechanisms to identify and subsequently eliminate clouds from images, thus uncovering previously concealed information and enhancing the quality of space data. The study emphasizes the necessity for further research aimed at refining cloud removal algorithms to accommodate diverse detection conditions and enhancing the overall efficiency of deep learning in satellite image processing. By highlighting potential benefits and advocating for ongoing exploration, the paper underscores the importance of advancing cloud removal techniques to improve data quality and unlock new applications in Earth remote sensing. In conclusion, the proposed approaches hold promise in addressing the persistent challenge of cloud cover in space imagery, paving the way for more accurate data analysis and future advancements in remote sensing technologies.
Digitalization of educational plays for quality education
Authors: Soulimani Younes Alaoui , Elaachak Lotfi , Bouhorma Mohammed
Published: August 2024

Repetitive tests on a learning material help schoolchildren to memorize and to learn this material. Psychologists call this phenomenon the testing effect. Skilled teachers use learning plays to embed routine tests in an engaging way. To widespread this practice, we propose a framework to digitize learning plays embedding routine tests into educational videogames. We have identified the smallest set of game design elements required to build an educational videogame out of a learning play. We have used the self-determination theory to group game design elements, and to define a breakdown structure for engagement engineering. This structure helps select the appropriate design elements for an engagement driver. We have applied the framework to digitize a learning play. We have tested the digital play with 238 schoolchildren who considered it as a video game. The video game tested a proposed pattern to create challenges allowing an engaging flow experience. The pattern increased responses (9%) and created time distortion (24%). Delivering rewards following variable schedules reduced errors (49%) and increased time distortion (16%). This research explores how to digitize learning plays into engaging educational video games and how to design engaging video games to remediate missed learning.
Developing decision-making serious games using Ren’Py visual novel engine
Authors: Hafizalshah Muhammad Hariz , Ghazali Aimi Shazwani , Sidek Shahrul Na’im
Published: August 2024

Serious games are effective tools defined as games designed with a focus on explicit utility rather than the generally construed notion of games purely as a source of entertainment. Decision-making games are a type of serious game that can be developed with the intent of studying behavior, educating, appraising or other similar applications that benefit through the information collected from the decision-making process. Digital versions of serious games are gaining prominence due to a higher level of interactivity and complexity, especially in Human-Agent Interaction (HAI) applications. The development of digital serious games generally extends beyond software developers, typically involving individuals from diverse backgrounds who may not possess the necessary programming skills required for the development process. The paper proposed the use of Ren’Py, an open-source visual novel game engine as a platform to develop decision-making games. The study examined the Ren'Py game engine’s potential through an assessment of the development process for the production of a decision-making serious game. Findings showed that Ren’Py satisfies the need for a relatively easy-to-develop platform for decision-making-based serious games due to its built-in systems that conform to currently applied serious decision-making game design principles.
Developing an effective focused crawler to retrieve data of Indian-origin scientists and utilizing text classification for comparative analysis
Authors: Gautam Shivani , Bhatia Rajesh , Jain Shaily
Published: August 2024

This article presents the implementation of focused web crawling to retrieve data about scientists of Indian ancestry who are working in foreign nations. This study demonstrates the effectiveness of web scraping in obtaining large amounts of data from publicly available online pages. The objective is to construct a collection of data pertaining to Indian scientists who are now employed in national laboratories overseas. Collecting a vast quantity of data on the aforementioned Indian scientists through manual search is a pointless task. Therefore, this study proposes a detailed plan for a focused web crawler that can gather similar data. Subsequently, we present a comprehensive assessment of numerous classification models on this newly created dataset. Our assessments indicate that the random forest model surpasses the other supervised models. The empirical findings on large datasets demonstrated that the combination of random forest with synthetic minority oversampling technique (SMOTE) and k-fold cross-validation methods yielded better performance compared to K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR) for Indian origin scientists. Conversely, SMOTE with an 80-20 random split demonstrated superior performance on smaller datasets. Overall, the random forest classifier demonstrated the most favorable outcomes, attaining a micro-average area under curve (AUC) of 90%. The outcomes of our study provide a solid foundation for further investigation into classification of text of Indian origin scientists.
Analyzing schools admission performance achievement using hierarchical clustering
Authors: Fahrudin Tora , Asror Ibnu , Wibowo Yanuar Firdaus Arie
Published: August 2024

In this study, an implementation of hierarchical clustering methods was conducted in schools’ admission data. We aim to demonstrate that the hierarchical clustering method can be used to help analyze the membership changes of each cluster based on its achievement number of new students from different months period observations. This method can be used by decision-makers to make a strategy for each school which has decreasing achievement from the previous period. In this paper, we employ the hierarchical clustering method to cluster admission performance achievement from fifty Telkom Schools. Instead of clustering admission in one period directly, this paper tried to analyze the movement of clustering membership from one period to another. We observed the movement membership of the group from three categories period, such as monthly, quarterly, and semesterly. The experimental results demonstrate that the monthly scenario was the best clustering result. The monthly scenario achieves the best score for all metrics such as the Dunn index, Silhouette score, Davies-Bouldin index, and Calinski-Harabasz compared to the quarter and semester scenario. There are four schools which are consistent in the first cluster and seven schools which are consistent in the second cluster in all scenarios and all periods.
Effective detection of breast pathology using machine learning methods
Authors: Orazayeva Ainur , Tussupov Jamalbek , Shangytbayeva Gulmira , Galymova Assem , Zhunissova Ulzhalgas , Tergeussizova Aliya , Tleubayeva Arailym , Kenzhebayeva Zhanat
Published: August 2024

This work is devoted to the research and development of methods for effectively identifying breast pathologies using modern machine learning technologies, such as you only look once (YOLOv8) and faster region-based convolutional neural network (R-CNN). The paper presents an analysis of existing approaches to the diagnosis of breast diseases and an assessment of their effectiveness. YOLOv8 and Faster R-CNN architectures are then applied to create pathology detection models in mammography images. The work analyzed and classified identified breast pathologies at six levels, taking into account different degrees of severity and characteristics of the diseases. This approach allows for more accurate determination of disease progression and provides additional data for more individualized treatment planning. Classification results at various levels can improve the quality of medical decisions and provide more accurate information to doctors, which in turn improves the overall efficiency of diagnosis and treatment of breast diseases. Experimental results demonstrate high accuracy and speed of image processing, providing fast and reliable detection of potential breast pathologies. The data obtained confirm the effectiveness of the use of machine learning algorithms in the field of medical diagnostics, providing prospects for the further development of automated systems for detecting breast diseases in order to improve early diagnosis and treatment efficiency.
Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business
Authors: Primawati Alusyanti , Sitanggang Imas Sukaesih , Annisa Annisa , Astuti Dewi Apri
Published: August 2024

The development of big data and real-time data warehouse (RTDW) technologies has transformed traditional business intelligence (BI) into real-time business intelligence (RTBI). The RTBI framework is developed in this study by incorporating machine learning-based real-time prediction features. The complexity of layer integration in the RTBI framework is a challenge in building RTBI. The development of RTBI was carried out in business areas that did not have RTBI from the beginning, such as the dairy goat milk business in Probolinggo, East Java. Another main reason is that the dairy goat milk business is a food alternative to cow's milk in Indonesia. The results of this study can contribute to increasing the potential value of the goat milk business. The research method was developed by adapting to the Kimball method and unified modeling language (UML). The real-time prediction feature with the long short-term memory (LSTM) algorithm is the main feature in the RTBI framework developed in the research. The calculation results of real-time predictive analysis latency successfully approached 0 milliseconds (ms), namely 9.35×10-5 ms. The application of RTBI in the dairy goat milk business was successfully built but the real data is very limited, so RTBI is less able to describe the movement of the business.
Stochastic agent-based models optimization applied to the problem of rebalancing bike-share systems
Authors: Soto Daniel Anderson , Ceballos Yony
Published: August 2024

This paper presents an agent-based model employing a stochastic optimization search that attempts to find an optimal solution to the online bicycle rebalancing problem for general bike-sharing systems. The algorithm receives the initial and final global state configuration of the system. The main objective of the study is to find the minimum cost path from the initial to the final state. Each agent of the model has four behavioral options that search the optimal configuration; at each iteration, it selects one of these options based on random thresholds and shares the temporary solution found with neighboring agents to improve their search process. The algorithm presents a high exploratory behavior of the search space, which helps to find an approximation away from the local optimal configuration. Additionally, the exchanges between agents allow a consensus on the solutions found. The algorithm has been tested with two different generated configurations using as a basis a real dataset extracted from a functional bike-sharing system collected in 2019. The results show a positive evolution originating from the emerging effect of stochastic selection and interaction between agents.
Deep learning model for diagnosing polycystic ovary syndrome using a comprehensive dataset from Kerala hospitals
Authors: Rao Divya , Dayma Riddhi Rajendra , Pendekanti Sanjeev Kushal , K. Aneesha Acharya
Published: August 2024

Polycystic ovary syndrome (PCOS) requires early and precise diagnosis to manage and prevent long-term health consequences effectively. In this research, a large dataset of healthcare data gathered from various hospitals in Kerala, India, was evaluated using multiple machine learning (ML) and deep learning (DL) models to identify a highly reliable and accurate prediction of PCOS. The six algorithms used for comparison with the proposed DL model are support vector classification, random forest, logistic regression, k-nearest neighbors, and gaussian naive Bayes; they were selected due to their strengths in handling features in large datasets. The highly parameterized neural networks were tuned using efficient approaches like Optuna and genetic algorithms. The results indicated that the model implemented using our proposed combination of DL model and Optuna, outperformed the traditional models, achieving 93.55% reliability. This suggests the potential for using deep learning for decision-making in diagnosing PCOS. This method demonstrates the importance of integrating various data types with powerful analytic tools in medical diagnostics to support customized therapy.
Geographic information system-based spatio-temporal detection and mapping of COVID–19 hot/cold spots in Oman
Authors: Al-Mulla Yaseen , Al–Muqaimi Mohammed , Ali Ahsan , Al-Badi Taif , Parimi Krishna , Chowdary Anusha
Published: August 2024

Infected COVID-19 patients, especially after March 11, 2020, grew drastically in Oman. Hence, a variety of measures were issued to restrict all social gatherings, commercial activities, and mandating preventative health practices. This study aimed to i) understand distribution patterns and impact of decisions and responses at the spread of confirmed cases; ii) highlight and verify most concentrated regions with infections; and iii) overview spatial changes of cases overtime. The analysis was carried out using inverse–distance-weighted interpolation and hotspot (Getis–Ord GI*) techniques. Results showed a substantial relationship between spatial structure of COVID–19 and population distribution and density. COVID–19 has increased by 11.5% weekly in the capital, which were locked down since April 2020. However, after health quarantine was lifted on May 29, 2020, weekly cases surged in the capital. Al-Batinah-North and Dhofar recorded an increase of 32.1% and 30.5%, respectively, after restrictions had eased. The analysis illustrated that spread of COVID–19 was shifting from Northeast to Southeast and later shifted back to the Northeast of the country at the end of year 2022. This study is beneficial for pertinent organizations to perform detailed studies for developing and monitoring disease systems and dominating relevant factors.
Detection and counting of wheat ear using YOLOv8
Authors: Mas Muhammad Sabri , Saidah Sofia , Ibrahim Nur
Published: August 2024

Detection and calculation of wheat ears are critical for land management, yield estimation, and crop phenotype analysis. Most methods are based on superficial and color features extracted using machine learning. However, these methods cannot fulfill wheat ear detection and counting in the field due to the limitations of the generated features and their lack of robustness. Various detectors have been created to deal with this problem, but their accuracy and calculation precision still need to be improved. This research proposes a deep learning method using you only look once (YOLO), especially the YOLOv8 model with depth and channel width configuration, stochastic gradient descent (SGD) optimizer, structure modification, and convolution module along with hyperparameter tuning by transfer learning method. The results show that the model achieves a mean average precision (mAP) of 95.80%, precision of 99.90%, recall of 99.50%, and frame per second (FPS) of 22.08. The calculation performance of the wheat ear object achieved accurate performance with a coefficient of determination (R^2) value of 0.977, root mean square error (RMSE) of 2.765, and bias of 1.75.
Detection of fungal diseases of plants from leaf images based on neural network technologies
Authors: Fedorchenko Ievgen , Yusof Mohd Faizal , Oliinyk Andrii , Chornobuk Maksym , Khokhlov Mykola , Alsayaydeh Jamil Abedalrahim Jamil
Published: August 2024

The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical implementation in agriculture and horticulture.
Machine learning-driven stock price prediction for enhanced investment strategy
Authors: Guennioui Omaima , Chiadmi Dalila , Amghar Mustapha
Published: August 2024

Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions.
A detailed analysis of deep learning-based techniques for automated radiology report generation
Authors: Dhamanskar Prajakta , Thacker Chintan
Published: August 2024

The automated creation of medical reports from images of chest X-rays has the potential to significantly reduce workloads for healthcare providers and accelerate patient care, especially in environments with limited resources. This study provides an extensive overview of deep learning-based techniques designed for radiology report generation from chest X-ray pictures automatically. By examining recent research, we delve into various deep learning architectures and techniques used for this task, including transformer-based approaches, attention mechanisms, sequence-to-sequence models, adversarial training methods, and hybrid models. We also discuss about the datasets used for evaluation and training, as well as future directions and research problems in this area. The significance of deep learning in revolutionizing radiology reporting is further emphasized by our review, which also highlights the need for additional research to address challenges such data accessibility, image quality variability, interpretation of complex findings, and contextual integration. The objective of this research is to present a comparative analysis of cutting-edge methods for developing automated medical report generation to enhance patient outcomes and healthcare delivery.
Photovoltaic power prediction using deep learning models: recent advances and new insights
Authors: Saad Basma , El Hannani Asmaa , Aqqal Abdelhak , Errattahi Rahhal
Published: August 2024

Artificial intelligence (AI) and its application across various domains have sparked significant interest, with each domain presenting distinct characteristics and challenges. In the renewable energies sector, accurate prediction of power output from photovoltaic (PV) panels using AI is crucial for meeting energy demand and facilitating energy management and storage. The field of data analysis has grown rapidly in recent years, with predictive models becoming increasingly popular for forecasting and prediction tasks. However, the accuracy and reliability of these models depend heavily on the quality of data, data preprocessing, model learning and evaluation. In this context, this paper aims to provide an in-depth review of previous research and recent progress in PV solar power forecasting and prediction by identifying and analyzing the most impacting factors. The findings of the literature review are then used to implement a benchmark for PV power prediction using deep learning models in different climates and PV panels. The aim of implementing this benchmark is to gain insights into the challenges and opportunities of PV power prediction and to improve the accuracy, reliability and explainability of predictive models in the future.
A fuzzy logic scheme based on spread rate and population for pandemic vaccine allocation
Authors: Kareem Abdul , Kumara Varuna
Published: August 2024

This paper deals with a novel decision-making scheme for inferring the allocation of vaccines to the provincial health care authorities by the central health care authority of a country in pandemic scenarios. This novel scheme utilizes a fuzzy logic-based inference scheme that utilizes the spread rate and population of a province as inputs to infer the vaccination rate. The proposed scheme is evaluated on the coronavirus disease (COVID-19) data from six southern states of India during the first week of October 2020, collected from the database maintained by the Government of India. The findings demonstrate that the suggested plan, which takes population and spread rate into account, makes sure that enough vaccination doses are distributed to the provinces with a larger spread rate with a higher priority, and that immunizations are not delayed in provinces with controlled spread rates. Also, in due course, all territories will appropriately distribute enough vaccine supply to control the spread. Therefore, this plan strengthens the efforts to control the pandemic outbreaks by ensuring the proper and balanced delivery of vaccines in a timely, efficient, and objective manner.
Phishing detection using grey wolf and particle swarm optimizer
Authors: Hamdan Adel , Tahboush Muhannad , Adawy Mohammad , Alwada’n Tariq , Ghwanmeh Sameh , Husni Moath
Published: August 2024

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.
Reddit social media text analysis for depression prediction: using logistic regression with enhanced term frequency-inverse document frequency features
Authors: Ayyalasomayajula Madan Mohan Tito , Agarwal Akshay , Khan Shahnawaz
Published: August 2024

Language provides significant insights into an individual’s emotional state, social status, and personality traits. This research aims to enhance depression detection through the analysis of linguistic features and various dataset attributes. The dataset, sourced from the social networking platform Reddit, comprises posts and comments from individuals diagnosed with depression. Logistic regression with term frequency-inverse document frequency (TF-IDF) is employed as the primary model for text classification. To improve model performance, a novel feature—the average time interval between consecutive posts or comments—is introduced, contributing to a marginal but noteworthy improvement in accuracy. The proposed model demonstrates superior F1 scores compared to other models applied to the same dataset. Given the increasing recognition of mental health’s significance, accurately diagnosing mental disorders is of paramount importance. This study underscores the potential of leveraging linguistic analysis and advanced machine learning techniques to identify depressive symptoms, thereby contributing to more effective mental health diagnostics and interventions.
2023 International Evidence‐Based Polycystic Ovary Syndrome Guideline Update: Insights From a Systematic Review and Meta‐Analysis on Elevated Clinical Cardiovascular Disease in Polycystic Ovary Syndrome
Authors: Tay Chau Thien , Mousa Aya , Vyas Aadhya , Pattuwage Loyal , Tehrani Fahimeh Ramezani , Teede Helena
Published: August 2024

Background

Polycystic ovary syndrome (PCOS) is the most prevalent female endocrinopathy. Although increased cardiovascular risk factors are well established for the syndrome, PCOS remains overlooked within the realm of cardiology. We conducted a systematic review and meta‐analysis on the risk of clinical cardiovascular disease (CVD) events in women with PCOS to inform the 2023 International Evidence‐Based PCOS Guideline.

Methods and Results

A systematic review and meta‐analysis was conducted comparing the risk of clinical CVD events in women with and without PCOS. Medline (Ovid), PsycInfo (Ovid), EMBASE, All EBM (Ovid), and CINAHL were searched from January 1, 2017, until March 1, 2023, to update the 2018 PCOS Guideline. Pooled odds ratios (ORs), incidence rate ratios (IRRs), and hazard ratios (HRs) were calculated. Twenty studies involving 1.06 million women (369 317 with PCOS and 692 963 without PCOS) were included. PCOS was associated with higher risk of composite CVD (OR, 1.68 [95% CI, 1.26–2.23]; I
2
= 71.0%), composite ischemic heart disease (OR, 1.48 [95% CI, 1.07–2.05]; I
2
= 81.0%), myocardial infarction (OR, 2.50 [95% CI, 1.43–4.38]; I
2
= 83.3%), and stroke (OR, 1.71 [95% CI, 1.20–2.44]; I
2
= 81.4%). The relationship with cardiovascular mortality was less clear (OR, 1.19 [95% CI, 0.53–2.69]; I
2
= 0%). Meta‐analyses of IRRs support these findings. Results from pooled HRs were limited by the small number of studies and significant heterogeneity.

Conclusions

This review provides evidence and highlights the importance of recognizing PCOS as a significant risk factor for CVD morbidity. The 2023 International Evidence‐Based PCOS Guideline now recommends awareness of increased CVD risk and comprehensive risk assessment in PCOS to help mitigate the burden of CVD in this common and high‐risk condition.
The genus Nemoura (Plecoptera: Nemouridae) from Shennongjia National Park of Hubei, China
Authors: LI MENGYU , BAKUMENKO OIHA , YANG DING , LI WEIHAI
Published: August 2024

Two new species of nemourid stonefly genus Nemoura, N. liuqifeii sp. nov. and N. shennongjia sp. nov., are described from Shennongjia National Park, Hubei Province of south-central China. The relationships between the new species and similar species in the genus are discussed. Three females of an undetermined Nemoura are also described from the park, however, they were not successfully associated with the new species or previously barcoded species.
Efficacy and safety of hydromorphone for cancer pain: a systematic review and meta-analysis
Authors: Alinejadfard Mohammadreza , Rajai Firouzabadi Shahryar , Mohammadi Ida , Oraee Soroush , Golsorkh Hossein , Mahdavi Sajjad
Published: August 2024

Background

Cancer pain significantly impacts individuals’ quality of life, with opioids being employed as the primary means for pain relief. Nevertheless, concerns persist regarding the adverse reactions and effectiveness of opioids such as morphine. Hydromorphone, recognized as a potent opioid, is a viable alternative for managing cancer-related pain. The goal of this systematic review and meta-analysis was to determine the effectiveness and safety characteristics of hydromorphone in comparison to other opioids, as well as different methods of administering this medication within the scope of cancer pain treatment.

Methods

The PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases were searched on December 25th, 2023. Following the PRISMA guidelines, a systematic investigation of databases was carried out, and suitable studies were chosen according to predetermined criteria (PICO framework). The meta-analyses were performed using a random-effects model.

Results

This review included 18 RCTs with 2271 patients who compared hydromorphone with morphine, oxycodone, or fentanyl, as well as other types of hydromorphone. Hydromorphone demonstrated efficacy similar to that of morphine and oxycodone in reducing cancer pain intensity, decreasing additional analgesic consumption, and improving quality of life. However, morphine showed slight superiority over hydromorphone in reducing breakthrough pain. Adverse events were comparable between hydromorphone and morphine or oxycodone. Patient-controlled and clinician-controlled hydromorphone administration routes yielded similar outcomes.

Conclusions

The outcomes of this study substantiate the efficacy of hydromorphone in the management of cancer-related pain, demonstrating similar levels of effectiveness and safety as morphine and oxycodone. These findings are consistent with prior comprehensive analyses, suggesting that hydromorphone is a feasible choice for alleviating cancer-associated pain. Additional investigations are warranted to determine its efficacy in distinct patient cohorts and for different modes of administration.

Trial registration

Prospero registration ID: CRD42024517513. Link: https://www.crd.york.ac.uk/PROSPERO/#recordDetails.
Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset
Authors: Lian Jie , Huang Fan , Huang Xinhai , Lau Kitty Yu-Yeung , Ng Kei Shing , Chu Carlin Chun Fai , Lam Simon Ching , Koohli-Moghadam Mohamad , Vardhanabhuti Varut
Published: August 2024

Background

Predicting an individual’s risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy.

Methods

The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions.

Results

The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups.

Conclusion

The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction.
Reversible cerebral vasoconstriction syndrome post-cardiac transplantation: a therapeutic dilemma: case report
Authors: Montarello Natalie L. , Irvine Iain , Warner Victoria , Hare James , Kaye David , Cloud Geoffrey C.
Published: August 2024

Background

Reversible cerebral vasoconstriction syndrome (RCVS) is characterized by diffuse, multifocal segmental narrowing of cerebral arteries and can result in ischaemic stroke. Causal factors, identified in 60% of cases, include immunosuppressant pharmacotherapy. The few reports following heart transplantation are almost all in Asian recipients. We report on a Caucasian Australian patient with immunotherapy induced RCVS post heart transplantation to highlight the state of knowledge of the condition and the treatment dilemma it poses.

Case presentation

A 51-year-old female underwent orthotopic heart transplantation at our institution. Induction immunotherapy comprised basiliximab, mycophenolate mofetil and methylprednisolone. On day 6 post-transplantation the patient was transitioned to oral prednisolone and tacrolimus. On day 7 the patient began to experience bilateral, severe, transient occipital and temporal headaches. On day 9 tacrolimus dose was up-titrated. A non-contrast computed tomography brain (CTB) was normal. Endomyocardial biopsy on day 12 demonstrated moderate Acute Cellular Rejection (ACR), which was treated with intravenous methylprednisolone. That evening the patient experienced a 15-minute episode of expressive dysphasia. The following morning she became confused, aphasic, and demonstrated right sided neglect and right hemianopia. A CT cerebral perfusion scan demonstrated hypoperfusion in the left middle cerebral artery (MCA) territory and cerebral angiography revealed widespread, focal multi-segmental narrowing of the anterior and posterior circulations. A diagnosis of RCVS was made, and nimodipine was commenced. As both steroids and tacrolimus are potential triggers of RCVS, cyclosporin replaced tacrolimus and methylprednisolone dose was reduced. A further CTB demonstrated a large left MCA territory infarct with left M2 MCA occlusion. The patient made steady neurological improvement. She was discharged 34 days post-transplantation with mild residual right lower limb weakness and persistent visual field defect on verapamil, cyclosporine, everolimus, mycophenolate mofetil and prednisolone.

Conclusion

Reversible cerebral vasoconstriction syndrome is rare after orthotopic heart transplantation. Until now, RCVS has been almost exclusively described in Asian recipients, and is typically caused by immunotherapy. The condition may lead to permanent neurological deficits, and in the absence of definitive treatments, early recognition and imaging based diagnosis is essential to provide the opportunity to remove the causal agent(s). Co-existent ACR, can pose unique treatment difficulties.