bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of cryptographic methods for ensuring security in the field of internet of things
Authors: Erlanovna Temirbekova Zhanerke , Muratovna Abdiakhmetova Zukhra , Sakhybay Tynymbayev
Published: January 2025

The number of internet of things (IoT) devices continues to grow, and so do the associated concerns regarding their security and privacy. Evaluating the efficacy of cryptographic solutions within IoT systems emerges as a crucial endeavor to uphold the integrity and reliability of these systems. Amidst the rapid evolution of IoT technology, safeguarding the confidentiality, integrity, and availability of data emerges as a top priority. This article underscores the significance of deploying robust cryptographic algorithms to fortify IoT devices against a myriad of potential threats. Effective evaluation of cryptographic solutions within IoT systems entails a comprehensive analysis and comparison of diverse algorithms, coupled with an assessment of their performance, resilience against attacks, and resource utilization. Central to evaluating the effectiveness of cryptographic solutions within IoT systems is a consideration of various factors including computational complexity, power consumption of devices, ease of implementation, and compatibility with existing infrastructures. This article reviews a number of cryptographic solutions including Rivest–Shamir–Adleman (RSA), El-Gamal, Paillier. These algorithms are implemented on the ATmega2560 microcontroller, which allows for a comprehensive assessment of key parameters such as efficiency in terms of encryption and decryption time, power consumption, and memory usage of IoT devices.
Marine scientific workflow execution resource provisioner in edge-cloud platform
Authors: Bagali Sheetal , Siddana Pradeep Kumar , C Poongothai , Patil Kavita Avinash , Purushothama Vani Bengaluru , Kumari P Ashwini
Published: January 2025

Workload application for deadline-intensive marine science, usually represented by directed acyclic graphs (DAGs), consists of interconnected operations that communicate huge quantities of information and operate on cutting-edge computational platforms. Massive communications of information among jobs running on separate computing servers, nonetheless, might come with substantial processing time, power consumption, and financial expenses. Therefore, there is room for further study of exchanging certain communications for computing to lower total interaction expenses. In addressing research this work introduces an effective resource provisioner for deadline-intensive scientific workload executer (ERP-DISWE) to reduce the makespan, consumption of energy, and cost of edge-computing platforms. The performance of ERP-DISWE is validated with the current resource provisioner using the sipht marine workload and shows superior performance concerning makespan, energy, and cost.
Leveraging 3D convolutional networks for effective video feature extraction in video summarization
Authors: Kadam Bhakti Deepak , Deshpande Ashwini Mangesh
Published: January 2025

Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Machine learning based optimized sea vessel location detection to identify illegal fishing
Authors: Kumar Ajay , Banerjee Kakoli , Kumar Pradeep , K. G. Harsha , P. Vinooth , Kumar Pankaj , Saumya Saumya , Nishant Nishant , Verma Satyam , Bhardwaj Vidushi
Published: January 2025

Illegal fishing is a pervasive and destructive global issue that poses a significant threat to maritime ecosystems and the resilience of fisheries. Illegal, unregulated, and unreported (IUU) fishing leads to the extinction of the fishing population. Many researchers have presented various approaches to detect illegal fishing, for example, using sensors, image recognition, and convolutional neural networks (CNNs) but each one has some limitations. Our research aims to compare different vessel gear types to select the best vessel container that can be easily monitored and less prone to illegal activities. To achieve this, our research proposed an optimization method that involves hyperparameter selection using a genetic algorithm instead of a grid search. Using the crossover method of the genetic algorithm our model is compatible with larger datasets and unknown search space which is not possible in the baseline algorithm i.e. grid search. Moreover, after applying the genetic hyperparameter optimization technique, the overall accuracy, recall, and F1 score is increased for all vessel types significantly. While comparing our optimized model with the existing model with different evaluation metrics, our model’s performance is outstanding.
Detecting network security incidents in wireless sensor networks using machine learning
Authors: Zhukabayeva Tamara , Buja Atdhe , Pacolli Melinda , Mardenov Yerik
Published: January 2025

This study enhances the domain of cybersecurity within wireless sensor networks (WSNs) through the integration of sophisticated artificial intelligence (AI) and machine learning (ML) techniques. By conducting an exploratory data analysis (EDA), this research reveals critical insights into network behavior, facilitating the development of predictive models for anomaly detection. The application of ML algorithms decision trees (DT) and random forest (RF) demonstrated dominant performance in identifying potential security threats, as evidenced by metrics accuracy, precision, recall, and F1 scores. This work not only enhances the security framework for WSNs but also contributes to the extensive field of network security, offering a robust analytical and predictive methodology for future cybersecurity initiatives. The advanced model can be deployed in other WSN and internet of things (IoT) based applications.
A novel dataset and part-of-speech tagging approach for enhancing sentiment analysis in Kannada
Authors: Eshwarappa Sunil Mugalihalli , Shivasubramanyan Vinay
Published: January 2025

The problem addressed in this research is the limited availability of labelled datasets and effective sentiment analysis tools for the Kannada language. Existing challenges include linguistic variations, cultural diversities, and the absence of comprehensive datasets designed specifically for sentiment analysis in Kannada. This research aims to enhance sentiment analysis capabilities for the Kannada language, addressing challenges posed by linguistic variations and limited labelled datasets. A novel Kannada dataset derived from SemEval 2014 task 4 was created using a conversion process. The dataset was processed using part-of-speech tagging, and a specialized model called K-BERT (Kannada bidirectional encoder representations from transformers) was introduced and implemented using Python within the Anaconda environment. Performance evaluation results showcased K-BERT's superiority over traditional machine learning (ML) algorithms and the BERT model, achieving an accuracy of 0.98, precision of 0.97, recall of 0.97, and F-score of 0.98 in sentiment classification for Kannada text data. This work contributes a unique Kannada dataset, introduces the K-BERT model specifically designed for Kannada sentiment analysis, and emphasizes the importance of collaborative efforts in advancing natural language processing (NLP) research for multilingual environments.
A new deep learning model based on convolutional neural network and residual blocks for driver drowsiness detection
Authors: Soultana Abdelfettah , Benabbou Faouzia , Sael Nawal , Bohsissin Soukaina
Published: January 2025

Recognizing the pivotal importance of monitoring driver inattention in the quest to minimize accidents and enhance safety and security, it is essential to highlight the inherent danger posed by drowsiness-a specific form of inattention that can significantly contribute to accidents. To address this issue, several propositions involving artificial intelligence have been put forth to effectively monitor and identify instances of driver drowsiness. However, challenges persist in the form of real-time processing constraints, the intricate nature of model parameters, and the response time of the model. The proposed methodology focuses on using convolutional neural networks (CNNs) and Residual blocks as robust and effective deep-learning models for the real-time detection of driver drowsiness. The integration of CNNs and Residual blocks enhances the model's precision, striking a well-balanced synergy between computational efficiency and performance. Notably, this approach demonstrated an impressive accuracy of 96.09% along with a recall, f1-score, and precision all at 96% when evaluated on the publicly available eye_dataset. Furthermore, the runtime of the developed model is a mere 70 ms. To further validate the efficiency of our proposed model, we conducted a comparative analysis with various pre-trained residual neural networks, including ResNet152, ResNet50, ResNet50v2, ResNet101, and ResNet101v2.
Evolving strategies in anti-phishing: an in-depth analysis of detection techniques and future research directions
Authors: Preeti Preeti , Sharma Priti
Published: January 2025

Phishing attacks are a major digital threat, impacting individuals and organizations globally. This review paper examines evolving anti-phishing strategies by analyzing five key techniques: URL blacklists, visual similarity detection, heuristic methods, machine learning models, and deep learning techniques. Each technique is evaluated for its mechanisms, unique features, and challenges. A systematic literature survey (SLR) is conducted to compare these methods; effectiveness. The paper highlights significant research challenges and suggests future directions, emphasizing the integration of artificial intelligence and behavioral analytics to combat evolving phishing tactics, this study aims to advance understanding and inspire more effective anti-phishing solutions.
Clustering and routing using spiral exploration mechanism with honey badger optimization in wireless sensor network
Authors: Lokesh Kumar Anitha Chikkanayakanahalli , Kamble Subhash , Vittal Rao Sanjay Kumar Naazre
Published: January 2025

Wireless sensor network (WSN) contains a huge number of spatially distributed sensor nodes that are connected by wireless to monitor and record information from the environment. The WSN nodes are battery-powered, thus reducing energy after a certain period which affects the network lifetime. To overcome this issue, this research proposed a spiral exploration mechanism with honey badger optimization (SEM-HBO) for cluster head (CH) and route path selection in WSN. The objective of this research is to reduce energy consumption and enhance network lifespan in WSN. The distance, communication cost, residual energy and cluster density are considered as fitness functions for selecting CH and route path in WSN. Through the SEM-HBO search behavior, it explores different routes and recognizes best one for reducing energy consumption and delays thereby enhancing network lifetime. The SEM-HBO performance is calculated based on packet delivery ratio (PDR), delay, energy consumption (EC), network lifetime (NL), and throughput for 100-500 nodes. The SEM-HBO performance is efficient and it achieves 99.62% and 99.59% of PDR for 100 and 200 nodes when compared to harmony search algorithm and competitive swarm optimization (HSA-CSO).
A novel hybrid model for sentiment analysis in MOOC forums with hybrid word and character-level neural networks
Authors: Jebbari Mohammed , Ouassil Mohamed Amine , Errami Mouaad , Rachidi Rabia , Hamida Soufiane , Cherradi Bouchaib , Raihani Abdelhadi
Published: January 2025

Sentiment analysis is crucial, in the field of natural language processing (NLP). Has applications in different areas. This study focuses on analyzing sentiments in massive open online course (MOOC) forums highlighting its importance in understanding how users interact and shaping educational strategies. The study presents a novel hybrid neural network model specifically tailored for sentiment analysis in MOOC forums. This innovative model combines word level and character level embeddings to handle the linguistic expressions commonly found in this context. The model architecture integrates bidirectional long short-term memory (BiLSTM) layers for word level embeddings and convolutional neural networks (CNNs) for character level embeddings aiming to harness the strengths of both types of embeddings for a view of the linguistic used in MOOC forum posts. Notably this model achieves an accuracy rate of 93.11% showcasing its effectiveness, in sentiment analysis within MOOC forums. This research contributes to sentiment analysis within the context of online education.
Communication induced checkpointing based fault tolerance mechanism using deep-learning in IoT applications
Authors: Lakshmi A Sowjanya , Ch Vanipriya
Published: January 2025

Internet of things (IoT) is increasingly used in diverse environments such as healthcare, industry and agriculture. They carry a risk of adverse effects if they make decisions based on faulty information. Software faults, especially transient faults are a primary contributor to deficient decision-making. The existing fault tolerant mechanisms often suffer from checkpoint overheads as checkpoints are placed in all the nodes. This paper describes a novel communication induced checkpointing based fault tolerance mechanism (CIC-FTM) designed to efficiently recover from transient faults, while minimizing useless and forced checkpoints. Long short-term memory (LSTM) based deep learning algorithm is used in our approach to predict fault occurrences and strategically place checkpoints. The proposed method also in turn improve system reliability and performance. Experimental results demonstrate the effectiveness of proposed CIC-FTM in IoT environment by minimizing the practicable operating time for checkpointing and back propagation, compared to traditional fault-tolerance mechanisms.
Quantitation of new arbitrary view dynamic human action recognition framework
Authors: Ho Anh-Dung , Doan Huong-Giang
Published: January 2025

Dynamic action recognition has attracted many researchers due to its applications. Nevertheless, it is still a challenging problem because the diversity of camera setups in the training phases are not similar to the testing phases, and/or the arbitrary view actions are captured from multiple viewpoints of cameras. In fact, some recent dynamic gesture approaches focus on multiview action recognition, but they are not resolved in novel viewpoints. In this research, we propose a novel end-to-end framework for dynamic gesture recognition from an unknown viewpoint. It consists of three main components: (i) a synthetic video generation with generative adversarial network (GAN)-based architecture named ArVi-MoCoGAN model; (i) a feature extractor part which is evaluated and compared by various 3D CNN backbones; and (iii) a channel and spatial attention module. The ArVi-MoCoGAN generates the synthetic videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint. These synthetic videos will be extracted in the next component by various three-dimensional (3D) convolutional neural network (CNN) models. These feature vectors are then processed in the final part to focus on the attention features of dynamic actions. Our proposed framework is compared to the SOTA approaches in accuracy that is extensively discussed and evaluated on four standard dynamic action datasets. The experimental results of our proposed method are higher than the recent solutions, from 0.01% to 9.59% for arbitrary view action recognition.
RNN Diabetic framework for identifying diabetic eye diseases
Authors: Albelaihi Arwa , Ibrahim Dina M.
Published: January 2025

Many areas of image identification and classification for medical imaging diagnostics have greatly benefited from deep learning (DL). Diabetic retinopathy (DR) will become the most common cause of blindness worldwide, making diabetes a major threat to public health. This research proposes an automated identification system using deep recurrent neural networks (RNNs) to identify and classify four categories of diabetic eye diseases: DR, cataract, glaucoma, and diabetic macular edema (DME). We use three different model architectures based on RNN and their types, we called our proposed system RNN Diabetic framework. These models are combined with one of the commonly used architectures that support sufficient accuracy and speed for the model which is residual network (ResNet)152V2. The three model architectures are RNN+ResNet152V2, gated recurrent unit (GRU)+ResNet152V2, and bidirectional GRU (Bi-GRU)+ResNet152V2. The proposed models were assessed as collected datasets: DIARETDB0, DIARETDB1, messidor, HEI-MED, ocular, and retina. A full analysis and evaluation of these three deep RNN architectures are presented. The experiments showed that the Bi-GRU+ResNet152V2 model worked better than the other two proposed models. In addition, we compare these three proposed models with the previous studies and find that the proposed Bi-GRU+ResNet152V2 model achieves the highest results with accuracy equal to 99.8%, 98.1% sensitivity, 98.6% specificity, 99.8% precision, 99.8% F1 score, and 99.8% areas under the curve (AUC).
A hybrid approach for hotspot problem using load balancing and advanced ant colony algorithm
Authors: Srikantha Padmini Mysuru , Kuzhalvaimozhi Sampath , Balasubramanya Susheela Shimoga , Buthello Sklita , Jagadish Sadana Hulugundi , Thammanna Spandana Mallegowdanakoppalu
Published: January 2025

Wireless sensor networks (WSNs) are crucial in various applications such as environmental surveillance, military operations, transportation monitoring, and healthcare. However, due to a finite set of sensor nodes' resources concerning energy, memory, disk, and CPU processing, nodes in WSNs often face hotspot issues. The sensor nodes that are located near the base station, are responsible for relaying data not only from themselves but also from neighboring nodes. This leads to hotspot issues, where nodes near the base station experience higher traffic loads and faster energy depletion. This paper mainly focuses on mitigating hotspot issues in heterogeneous WSNs using unequal clustering, load balancing, and an advanced ant colony algorithm. This approach involves devising strategies for selecting cluster heads, determining clusters optimal number and formation, and optimizing data transmission processes. Central to the methodology is utilizing load balancing mechanisms and an advanced ant colony algorithm to distribute the workload among sensor nodes more evenly and find the optimum routing path. The proposed algorithm shows promise in alleviating traffic congestion and energy depletion and provides an innovative approach to enhance network performance and prolong the lifespan of sensor nodes.
Machine learning models in renewable energy forecasting: a systematic literature review
Authors: Rhafes Mohamed Yassine , Moussaoui Omar , Raboaca Maria Simona
Published: January 2025

During the past years, the convergence of machine learning (ML) technologies with renewable energy sectors has become a significant key area of innovation as a key area of innovation, enhancing the efficiency and predictability of sustainable energy sources. ML algorithms, adept at handling complex data, have become essential in forecasting energy outputs from variable sources like solar and wind. This integration has led to the development of smarter, more adaptive grid systems, capable of efficiently managing the variability of renewable energy sources. This review paper focuses on several key areas: firstly, it provides a summary of related work, specifically focusing on ML in the renewable energy field. Secondly, it delves into ML models and evaluation metrics used for solar and wind energy forecasting. Thirdly, it analyzes 21 studies published from 2019 to 2023, primarily centered on solar energy (60%) and wind energy (40%), with an emphasis on various forecasting horizons, highlighting the results of the ML algorithms used and the performance metrics to evaluate their effectiveness. Finally, it identifies gaps and opportunities in this field. The state-of-the-art review and its findings can offer a solid foundation for future research initiatives.
Forecasting virtual machine resource utilization in cloud computing: a hybrid artificial intelligence approach
Authors: Doukha Rim , Ez-Zahout Abderrahmane , Ndayikengurukiye Aristide
Published: January 2025

Cloud computing has transformed the management of IT infrastructures by providing scalable, flexible, and cost-effective solutions. However, efficient resource management in cloud environments remains a significant challenge, as over-provisioning or under-provisioning of resources can lead to unnecessary costs or degraded performance. Accurate forecasting of virtual machine (VM) resource utilization is crucial for optimizing resource allocation, reducing operational expenses, and ensuring compliance with service level agreements (SLAs). This study aims to address these challenges by developing a hybrid forecasting model that combines the strengths of auto regressive integrated moving average (ARIMA), linear regression (LR), and long short-term memory (LSTM) techniques. By integrating these methods, our model provides more accurate predictions and better adaptability to various workload patterns, helping cloud service providers and users to make informed decisions about resource allocation, ultimately reducing costs. The data was collected from multiple EC2 instances and processed using amazon web services (AWS) Glue with Spark. The experimental results demonstrate that the hybrid model outperforms individual models such as ARIMA, LR, and LSTM in terms of accuracy for forecasting memory, CPU, and disk utilization, offering a more effective solution for managing cloud resources efficiently.
Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network
Authors: Angadi Sanjeevkumar , Tripathi Mukesh Kumar , Sukte Chudaman Devidasrao , Shivendra Shivendra
Published: January 2025

Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by diverse imaging modalities and temporal variations. The problem involves effectively registering CT and MRI images, followed by inhale and exhale classification. The proposed approach begins with feeding the input images into a convolutional neural network (CNN), followed by applying a deformation field to generate an intermediate output (output-1). This output, along with the input MRI images, is further processed by a CNN to produce output-2. Subsequently, output-2 and the input MRI image are subjected to another CNN, resulting in the final registered image. The classification phase utilizes a DMN optimized by the SARSO algorithm, which combines smell agent optimization (SAO) and rat swarm optimizer (RSO). The results demonstrate that SARSO-DMN achieves a maximum accuracy of 90.7%, a minimum false positive rate (FPR) of 11.3%, and a maximum true positive rate (TPR) of 91.2%. The SARSO-DMN approach provides a robust solution for MIR and classification, leveraging advanced optimization techniques to enhance performance.
A case study on the causal of electric two-wheelers traffic accidents in Yangzhou, China
Authors: Ruijie Wang , Fadilah Suzi Iryanti , Mohd Shariff Azizul Rahman , Mohd Zaini Khuzairi , Keikhosrokiani Pantea , Ning Wang
Published: January 2025

In recent years, electric two-wheelers are a very popular means of transportation in China and are loved by the people. And, because driving electric two-wheelers does not require a driver's license or insurance, electric two-wheelers have become the preferred mode of transportation for the people in China. However, while electric two-wheelers bring convenience to people, they also bring hidden dangers to traffic safety. Nearly 200,000 traffic accidents occur in China every year, of which electric two-wheelers account for 60%. In addition, the trend of traffic accidents is increasing year by year, which is seriously threatening the safety of people's lives and property. It is urgent to prevent the traffic accidents of electric two-wheelers and maintain the personal safety of citizens. Therefore, this study aims to determine which factors contribute to traffic accidents of electric two-wheelers and to explore the importance of their influence on traffic accidents. The sample data for this study will be collected from the people of Yangzhou City, Jiangsu Province, China by distributing questionnaires. The data from the completed questionnaires will also be analyzed using SPSS analysis tool.
Synergistic ensemble classification framework: utilizing a soft voting algorithm for enhanced prediction and diagnosis of diabetes mellitus
Authors: Lohani Bhanu Prakash , Dagur Arvind , Shukla Dhirendra Kumar
Published: January 2025

Diabetes, a serious condition characterized by elevated blood glucose levels, can be effectively identified, and predicted early using machine learning (ML) algorithms. The research provides a comprehensive assessment of three ensemble ML models-stacking, soft voting, and hard voting-focused on enhancing diabetes diagnosis among Pima Indian women dataset taken from the National Institute of Diabetes and Digestive and Kidney Diseases, this study focuses on Pima Indian women aged 21 and older, with the dataset comprising critical diagnostic measurements. Two ensemble models were developed and evaluated on various evaluation parameters. The stacking model combines predictions from various classifiers using a meta-classifier, leveraging their strengths for final decision-making. In contrast, the voting model aggregates probability estimates from each classifier, providing nuanced predictions. Both models were rigorously evaluated on a validation dataset, emphasizing accuracy, specificity, sensitivity, and the receiver operating characteristic (ROC) area under the curve (AUC). Notably, the voting-based ensemble methods demonstrated superior performance in predicting diabetes for this cohort. However, their effectiveness heavily relies on preprocessing, base model selection, and hyperparameter optimization. This study underscores the potential of ensemble models in medical diagnostics, highlighting the critical role of data preprocessing, and configuration in enhancing predictive accuracy.
Utilizing logistic regression in machine learning for categorizing social media advertisement
Authors: Gonaygunta Hari , Nadella Geeta Sandeep , Meduri Karthik
Published: January 2025

The purpose of this paper is to investigate the use of logistic regression in machine learning to distinguish the types of social media advertisements. Since the logistic regression algorithm is designed to classify data with a target variable that has categorical results, it is the one selected. As a result, this research intends to measure the efficiency of logistic regression for the classification of social media advertisements. This research centers on the social media advertisements dataset and employs logistic regression for classification purposes. The model is evaluated against performance metrics to measure the extent to which it can categorize social media advertisements. As a result, the findings of this study show that logistic regression is fit for classifying social media advertisements. Logistic regression is important for machine learning when it comes to classifying social media advertisements because it supports categorizing advertisements according to their characteristics and precisely predicts the categorical results.
Marrying deep learning within blockchain technology for credit card fraud prevention
Authors: Karkaba Imane , Adnani El Mehdi , Erritali Mohammed
Published: January 2025

Over the last decade or so, an excessive turnout on e-financial transactions by companies and customers results in a pinnacle growth of credit card fraudulent acts, leading them to lose frequently huge amounts of money. In their trial to find the key to this issue, specialists and experts have founded a bunch of fraud detection and prevention models relied on data mining, machine learning and deep learning. Yet, the outcomes were not effective nor optimal. Thereupon, to foster these prototypes’ function, Blockchain -a safe, decentralized and unchangeable database- was deployed to ban any sort of anomaly or data alteration after storing. For identifying malicious financial behaviours, our work managed to intermingle a pre-designed deep learning prototype with Blockchain. That is to say -for the sake of preventing fraudulence that concerns credit card- we applied the former prototype in Blockchain system. Still, Blockchain showed impotence in terms of using off-chain data, which embeds deep learning pattern, specifically through smart contract. Hence, we activated chainlink boosting our model to surmount this obstacle.
Advancements in seismic data collection and analysis through machine learning
Authors: Kulkarni Sujata , Phadke Malay , Sawant Ashwini , Patel Neel , Patil Om
Published: January 2025

The evolution of seismic data collection has been driven by the need for stations to capture large volumes of high-frequency signals continuously. These signals typically contain both seismic and non-seismic information. Previous research converted SEED data into CSV format and used principal component analysis (PCA) for feature extraction from the seismic dataset. Machine learning models were then employed, showing an improvement in identifying seismic and non-seismic events. This paper focuses on applying deep learning methods, specifically deep neural networks (DNN) and a hybrid model combining long short-term memory (LSTM) networks with DNN (LSTM+DNN). The proposed deep learning models demonstrate a notable improvement over traditional machine learning technique. Experimental results show a test accuracy of 99.24% using deep learning, compared to an average of 97.80% achieved with machine learning models, indicating a 1.46% enhancement in detection accuracy. This underscores the potential of deep learning in accurately detecting seismic events in real-time monitoring systems.
Unveiling perceptions: aspect-based sentiment analysis of Malaysia’s e-hailing reviews
Authors: Abu Samah Khyrina Airin Fariza , Amit Mohamad Azan , Salleh Siti Salwa , Latif Wannoraini Abdul , Kamarudin Nurul ‘Azwa , Saad Shahadan , Hasrol Jono Mohd Nor Hajar
Published: January 2025

The growing demand for e-hailing services in Malaysia leads to increased competition among more than 20 licensed e-hailing service providers. Consumer satisfaction is a crucial factor influencing variables in the business organization, and understanding consumers’ perceptions is vital for service improvement. Reviews on e-hailing services are unstructured data and massive, making comparisons difficult. Thus, this study aims to classify Malaysia’s e-hailing service reviews from Google Play Store and X using latent Dirichlet allocation (LDA) and support vector machine (SVM). Aspect-based sentiment analysis (ABSA) was performed using a two-staged method, applying LDA for aspect category detection and SVM for aspect sentiment classification separately. Fare, availability, comfort, time, and convenience are five predetermined aspect categories in this study. The LDA for English and Malay achieved a perplexity of -7.31 and -7.49, respectively. Besides, the accuracy scores of SVM for English and Malay are 86.32% and 62.97%, respectively.
Analysis of similarity index between iThenticate and Ouriginal plagiarism detection software: a comprehensive study
Authors: Rahman Md. Hamidur , Danno Bayissa Leta , Wase Mola Dessalegn , Islam Muhammad Shahidul , Hussain Andrabi Syed Murtaza , Reza Md. Nasim , Shaw Dhananjoy
Published: January 2025

Intellectual property plagiarism is increasingly prominent in contemporary society, involving the unethical practice of claiming someone else's ideas, words, or creative works without proper acknowledgment. This study aimed to compare the performance of iThenticate and Ouriginal plagiarism detection software by analyzing their similarity index. Twenty original manuscripts (N=20) were examined for content similarity, with each manuscript analyzed first with Ouriginal and then with iThenticate. The focus was on comparing the two tools based on matched sources, word matches, and overall similarity index percentage. Data analysis using SPSS v26 included descriptive statistics, an independent t-test, correlation, and ranking of the similarity percentages, with significance set at p<0.05. The results indicated no significant differences in matching sources, matching words, or similarity index (p>0.05) between iThenticate and Ouriginal.
 A strong positive correlation (r=.758, p<.000) was observed between the similarity indices of the two software programs. The analysis of the low similarity range (≤10%) also revealed no statistical significant difference (p>.05). However, the mean similarity percentage detected by iThenticate was higher at 11.40%, compared to 6.85% for Ouriginal. Based on the findings, both iThenticate and Ouriginal demonstrated comparable effectiveness in detecting plagiarism, highlighting their importance in curbing academic dishonesty and protecting intellectual property rights.
Systemic Circulation in Advanced Heart Failure and Cardiogenic Shock: State-of-the-Art Review
Authors: Hungerford Sara L. , Everett Kay D. , Gulati Gaurav , Sunagawa Kenji , Burkhoff Daniel , Kapur Navin K.
Published: January 2025

The integrative physiology of the left ventricle and systemic circulation is fundamental to our understanding of advanced heart failure and cardiogenic shock. In simplest terms, any increase in aortic stiffness increases the vascular afterload presented to the failing left ventricle. The net effect is increased myocardial oxygen demand and reduced coronary perfusion pressure, thereby further deteriorating contractile function. Although mechanical circulatory support devices should theoretically work in concert with guideline-directed medical therapy, cardiac resynchronization and inotropic and vasopressor agents designed to support myocardial performance and enhance left ventricle recovery, this does not always occur. Each therapy and intervention may result in vastly different and sometimes deleterious effects on vascular afterload. Although best described by a combination of both steady-state and pulsatile components, the latter is frequently overlooked when mean arterial pressure or systemic vascular resistance alone is used to quantify vascular afterload in advanced heart failure and cardiogenic shock. In this state-of-the-art review, we examine what is known about vascular afterload in advanced heart failure and cardiogenic shock, including the use of temporary and permanent mechanical circulatory support systems. Importantly, we outline 4 key components for a more complete assessment of vascular afterload. Unlike previous discussions on this topic, we set aside considerations of venous return and ventricular preload, as important as they are, to focus exclusively on the hydraulic load within the systemic circulation against which the impaired left ventricle must contract.
Plasma SVEP1 Levels Predict Cardiovascular Events in Hypertrophic Cardiomyopathy Beyond Conventional Clinical Risk Models Including NT-proBNP
Authors: Osawa Itsuki , Akita Keitaro , Hasegawa Kohei , Fifer Michael A. , Tower-Rader Albree , Reilly Muredach P. , Maurer Mathew S. , Stitziel Nathan O. , Javaheri Ali , Shimada Yuichi J.
Published: January 2025

BACKGROUND:

Hypertrophic cardiomyopathy is the most common genetic cardiomyopathy and causes major adverse cardiovascular events (MACE). SVEP1 (Sushi, von Willebrand factor type A, epidermal growth factor, and pentraxin domain containing 1) is a large extracellular matrix protein that is detectable in the plasma. However, it is unknown whether adding plasma SVEP1 levels to clinical predictors including NT-proBNP (N-terminal pro-B-type natriuretic peptide) improves the prognostication in patients with hypertrophic cardiomyopathy.

METHODS:

We performed a multicenter prospective cohort study of 610 patients with hypertrophic cardiomyopathy. The outcome was MACE defined as heart failure hospitalization or cardiac death. In 4 groups stratified by the median levels of SVEP1 and NT-proBNP, we compared the risk of MACE using the Cox proportional hazards model adjusting for 15 clinical predictors. We also developed a Lasso-regularized Cox proportional hazards model to predict time to first MACE by adding SVEP1 to the 15 clinical predictors with or without NT-proBNP and compared the predictive performance based on C statistics using 10-fold cross-validation.

RESULTS:

Even in the low NT-proBNP groups, the high SVEP1 group had higher risks of MACE compared with the low SVEP1 group (adjusted hazard ratio, 4.52 [95% CI, 1.05–19.4];
P
=0.042). In predicting time to first MACE, the addition of SVEP1 improved the C statistics of the clinical plus NT-proBNP model (0.87 [0.83–0.91] versus 0.82 [0.78–0.86];
P
=0.01). The clinical plus SVEP1 model also outperformed the clinical plus NT-proBNP model (0.86 [0.82–0.91] versus 0.82 [0.78–0.86];
P
=0.04).

CONCLUSIONS:

SVEP1 improved the predictive performance of conventional models, including known clinical parameters with or without NT-proBNP, to predict future MACE in patients with hypertrophic cardiomyopathy.
The Nature, Ethics, and Politics of Uncivil Obedience
Authors: Kling Jennifer
Published: January 2025

Uncivil obedience, also sometimes called malicious compliance, has the potential to be a galvanizing force for political change. Historically, it played a key role in many 20th century labor movements, and is still used today by both individuals and more organized activist groups. Despite this, uncivil obedience is less often a topic of philosophical discussion than its more well-known cousin, civil disobedience. In particular, uncivil obedience’s relationship to violence is almost entirely unexplored. In this paper, I outline the necessary conditions for some act to count as uncivil obedience, discuss its relationship to violence, and then present the conditions that must be met for uncivil obedience to be justified. Like other forms of protest and resistance, uncivil obedience has normative limits; there are things we ought not do with it, even in the name of fighting for a better world.
Conidiobolus srinivasanii (Conidiobolaceae, Entomophthorales), a novel species from Western Ghats, Maharashtra, India
Authors: PAWAR KADAMBARI S. , SINGH PARAS N. , SINGH SANJAY K.
Published: January 2025

The genus Conidiobolus represents fungi of ecological and pathological importance, recently undergoing significant taxonomic revisions driven by molecular advancements. This study describes Conidiobolus srinivasanii sp. nov., a saprobic species isolated from plant litter in Maharashtra, India. Morphological examinations reveal unique features, such as vasiform primary conidiophores, catenate secondary conidia, and multipolar germ tube development, distinguishing it from closely related species. Multigene phylogenetic study (TEF-1α, mtSSU, nucLSU) substantiates its placement within Conidiobolus s.s. while emphasizing genetic divergence from other species. This work integrates morphological observations with genomic data to improve understanding of the taxonomy of this newly described species and underscores the significance of plant litter as a crucial habitat for fungal diversity. These findings aid in the continual refinement of fungal classification and elucidation of the evolutionary complexity of the genus.
Revisiting the endemic Eugenia grandifolia (Myrtaceae), a rare species from the Brazilian state of Rio de Janeiro: extended description and conservation
Authors: FERNANDES THIAGO , BRAGA JOÃO MARCELO ALVARENGA , VALDEMARIN KARINNE SAMPAIO , DENARDI VINÍCIUS LUVIZOTTO , PRIETO PABLO VIANY , MAZINE FIORELLA F.
Published: January 2025

This paper documents the rediscovery of Eugenia grandifolia, an endemic species from the Brazilian state of Rio de Janeiro that was primarily known from 19th century collections. Eugenia grandifolia is remarkable by its relatively large, subsessile leaves with cordate bases, inflorescences in racemes and often ramiflorous, and fruits with up to seven seeds. We provide herein a second step lectotypification, updated morphological description, notes on taxonomy, distribution, habitat, and phenology of the species. The presence of colleters in the species is first recorded here. Its fruits, previously unknown to science, are illustrated and described herein, and the first photographic documentation of the live plant in its habitat is also presented. Finally, we present a distribution map and a reassessment of the conservation status of E. grandifolia.
Rhododendron yuanbaoshanense (Ericaceae), a new species from Guangxi, China
Authors: DENG YI-HUI , PAN BO , DING TAO , LIU HONG , QIN XUE-LIAN , HUANG ZI-XIONG , DENG MIN-MIN
Published: January 2025

A new species of Rhododendron (Ericaceae) in subgen. Hymenanthes, sect. Ponticum, subsect. Fortunea from Yuanbaoshan, northern Guangxi, China, namely R. yuanbaoshanense, is described and illustrated. The new species is most similar to R. yuefengense and R. orbiculare, but it differs from R. yuefengense by its broader ovate to orbicular leaf blades with a cordate-auriculate base, cylindric petioles, and longer inflorescence rachis. It is distinguished from R. orbiculare in having 8–17-flowered inflorescences with longer rachis, funnelform-campanulate to campanulate corollas that are pink to pale pinkish-purple and sparsely glandular outside, and densely white glandular style.
Atmospheric Circulation Patterns Conducive to Summer Extreme Temperature, Precipitation, and Vapor Pressure Deficit Events over Northeast China
Authors: Li Xingxing , Sun Jianqi
Published: January 2025

Based on daily observational and reanalysis datasets during 1979–2021, we classify summer atmospheric circulations over northeast China (NEC) into nine circulation patterns (CPs; CP1–CP9) by a self-organizing map (SOM) neural network and link extreme temperature, precipitation, and vapor pressure deficit (VPD) events to typical CPs. The results show that more extreme temperature events occur under the high pressure pattern of CP1 (CP2), which is connected with the northward displacement of the western Pacific subtropical high and the northeastern Asia ridge to the north (south), consequently yielding warm advection and a warmer background for extreme temperature in the whole (southeastern) NEC region. More extreme precipitation events occur under the low-vortex pattern of CP4 (CP7), which is affected by deepened midlatitude troughs and reflects northwestern (central) cyclone activities. The resulting southeasterlies transport moisture from northeastern Asia and the Yellow Sea–Japan Sea regions, providing favorable dynamic and moisture conditions for extreme precipitation in northern-central (southeastern) NEC. The strong Asian polar-vortex pattern of CP6 (CP9) is associated with more extreme VPD events because it induces southern positive and northern negative pressure anomalies (negative pressure anomalies) and warm-dry (cold-dry) airflow in NEC, which results in an increase in the VPD anomalies and promotes drier air in eastern-central (southern) NEC. Furthermore, the highly correlated temporal changes in the frequency of extreme events and CPs confirm that CPs could partly explain the temporal variations in NEC extremes. This study enhances our understanding of NEC extreme events and provides a scientific basis for their forecasting.
Relationship of Decadal Variations in East Asian Winter Monsoon and Northwestern North Pacific SST in CMIP6 Preindustrial Long-Term Simulations
Authors: Gao Yuan , Sun Jianqi , Yu Shui , He Shengping
Published: January 2025

East Asian winter monsoon (EAWM) plays an important role in people’s livelihoods, society, and natural environment over East Asia. Previous studies indicate that observed EAWM presents an obvious decadal variation. However, due to the limited duration of observed data and the coexistence of climate internal variability and external forcings, a comprehensive understanding of the decadal EAWM variation and its underlying mechanism remains challenging. In this study, we therefore investigate the EAWM and northwestern North Pacific SST (NPSST) decadal variation and their relationships in long-term preindustrial control (piControl) simulations from 30 coupled models, within phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the piControl simulations in 30 coupled models can well reproduce the observed decadal EAWM variation which exhibits a 10–30-yr period. Possible mechanism analysis suggests that, on the decadal time scale, when the northwestern NPSST is warmer than normal, the overlying atmosphere is heated by SST-related diabatic heating. Consequently, the meridional temperature gradients are weakened to the south of the warm SST region but enhanced to the north, inducing a meridional dipole pattern of zonal wind anomalies and weakening the East Asian trough. Therefore, the EAWM is weakened and warmer winters occur over East Asia. In turn, the atmospheric feedback could favor maintaining the SST anomaly. Such a mechanism is consistent in the piControl simulations. In addition, the decadal relationship between the EAWM and northwestern NPSST is stable in the simulation periods. Such results further imply that the decadal EAWM variation originates primarily from climate internal variability and northwestern NPSST anomaly could play an important role.

Significance Statement

EAWM presents an obvious decadal variation, significantly impacting the East Asian climate. However, the limited duration of observed data impedes a comprehensive study of the decadal EAWM variation and it is difficult to distinguish the influence of climate internal variability and external forcings from the observations. Therefore, we use long-term piControl simulations without natural and anthropogenic forcings in 30 CMIP6 models for further investigation in this study. The piControl simulations can reproduce the decadal variation of the EAWM with a 10–30-yr period and the stable relationship between EAWM and northwestern North Pacific SST on the decadal time scale. The result indicates that the EAWM decadal variation originates primarily from climate internal variability, in which the northwestern North Pacific SST could play an important role.