bluuebunny/crossref_metadata_2025_split huggingface dataset:
ОСОБЕННОСТИ УЧЕТА ЭКСПЛУАТАЦИОННЫХ НАГРУЗОК
ПРИ ОБЕСПЕЧЕНИИ МЕХАНИЧЕСКОЙ БЕЗОПАСНОСТИ КОНСТРУКЦИЙ ПОКРЫТИЯ ОБЪЕКТА СТРОИТЕЛЬСТВА
Authors: Gurova Yelena Vladimirovna , Kuramshin Renat Khosyainovich , Korotkova Anastasiya Aleksandrovna , Levshin Georgiy Igorevich , Mashakaryan Ararat Sergeyevich 
Published: April 2024

Проведена оценка влияния особенностей режима эксплуатации объекта незавершенного строительства на параметры
механической безопасности конструкций покрытия. Выполнен анализ влияния негативных внешних воздействий, в частности, повышенных пылевых отложений, на несущую способность элементов покрытия в сложившихся условиях эксплуатации. Разработан вариант усиления конструкций покрытия объекта строительства с изменением расчетной схемы его элементов, который позволяет довести параметры механической безопасности элементов покрытия до нормативных значений
и обеспечить дальнейшую безопасную эксплуатацию объекта.
ДИСПЕРГИРОВАНИЕ ДИЗЕЛЬНОГО ТОПЛИВА В ВЫСОКОВОЛЬТНОМ ЭЛЕКТРОСТАТИЧЕСКОМ ПОЛЕ
Authors: Svintsov Vladimir Yakovlevich , Arabov Mikhail Shugeyevich , Prokin Sergey Vladimirovich 
Published: April 2024

В статье приведены результаты исследования диспергирования дизельного топлива за счет энергии высоковольтного
электростатического поля. Процесс диспергирования фиксировался с помощью цифровой фототехники, что позволило определить размеры частиц как функцию напряжения питания высоковольтного блока экспериментальной установки.
СОВРЕМЕННЫЕ ТЕНДЕНЦИИ В АРХИТЕКТУРЕ АЛТАРНОЙ ПРЕГРАДЫ ХРАМОВ Г. БАРНАУЛА
Authors: Savenkova Olga Vladislavovna , Pomorov Sergey Borisovich 
Published: April 2024

В статье рассмотрены современные тенденции в архитектуре алтарной преграды. Объектом изучения являются иконостасы в храмах г. Барнаула, возведенных за последние тридцать лет. Авторами была собрана информация о более тридцати храмах, находящихся в городе Барнауле. Собран фотоматериал для описания архитектурно-художественных и символических
особенностях иконостасов. Приводятся примеры деревянных, мраморных, фаянсовых и кованных алтарных преград в современных православных храмах. Алтарные преграды были классифицированы по материалам, типам и количеству ярусов. Рассмотрены тенденции конструкций современных иконостасов. Результаты исследования работы могут быть использованы для создания проектов и строительства новых иконостасов, а также для образовательной и исследовательской деятельности.
Attention-Based Multi-Layered Encoder-Decoder Model for Summarizing Non-Interactive User-Based Videos
Authors: Charul Bhatnagar Vasudha Tiwari, 
Published: April 2024

Video summarization extracts the relevant contents from a video and presents the entire content of the video in a compact and summarized form. User based video summarization, can summarize a video as per the requirement of the user. In this work, a non interactive and a perception-based video summarization technique is proposed that makes use of attention mechanism to capture user’s interest and extract relevant keyshots in temporal sequence from the video content. Here, video summarization has been articulated as a sequence-to-sequence learning problem and a supervised method has been proposed for summarization of the video. Adding layers to the existing network makes it deeper, enables higher level of abstraction and facilitates better feature extraction. Therefore, the proposed model uses a multi-layered, deep summarization encoder-decoder network (MLAVS), with attention mechanism to select final keyshots from the video. The contextual information of the video frames is encoded using a multi-layered Bidirectional Long Short-Term Memory network (BiLSTM) as the encoder. To decode, a multi-layered attention-based Long Short-Term memory (LSTM) using a multiplicative score function is employed. The experiments are performed on the benchmark TVSum dataset and the results obtained are compared with recent works. The results show considerable improvement and clearly demonstrate the efficacy of this methodology against most of the other available state-of-art methods.
Research and Application of Active Security Protection Methods for Automotive Cloud
Authors: Qiang Zhang, Baizheng Wang Kexun He, 
Published: April 2024

With the gradual maturity of the Internet of vehicles and cloud computing industry and the continuous evolution of network attack forms, the demand for cloud security in the automotive industry will increase year by year. In the automobile cloud platform environment, because the traditional security scheme is still effective for the north-south traffic, the security problem of the automobile cloud platform mainly lies in the security protection of the east-west traffic of the platform and the division of the security boundary. The purpose of this project is to solve the security risk of east-west cross-subnet traffic of the automobile cloud platform, especially the traffic security problem between hosts, in order to provide the next generation firewall, intrusion prevention and other professional security protection functions for the virtual network environment without affecting the service virtual machine. In order to achieve the above purpose, it is necessary to solve the problem of virtual switch diversion and virtual machine drift on the cloud platform, study the service security requirements of the automotive cloud platform, the relationship between data transfer and processing on the cloud platform, and finally provide a prototype system of active security protection for the cloud platform risks. The system implements VM microisolation, network attack defense, malicious code defense, ip address-based secure access policies, application-type secure access policies, and VM migration security functions in cloud application scenarios.
Stress-Nets- A Novel LSTM Ensembled Single Feed Forward Layers for Stress Classification with EEG Signals
Authors: S. Jerritta Muhammadu Sathik Raja M. S., 
Published: April 2024

Mental instability and emotional imbalance of the individual can be reflected in the form of stress which results in an inappropriate work ethics. There are various methods for the Stress creation. Moreover , several bio-signal sources such as Electroencephalograph(EEG), Electrocardiography(ECG) and Electromyography(EMG) are considered to be the important catalyst for developing the stress detection systems(SDS). Recently, machine and deep learning algorithms has gained more popularity in designing the SDS using the bio-signals. Further, EEG based SDS with ML and DL algorithms plays an important role for its high classification accuracy and performance. However, these EEG based SDS systems needs the better lime light of improvisation in terms of performance and computational overhead to deal with multiple datasets. In this context, this manuscript proposes new Long Short term Memory recurrent units(LSTM) for extracting temporal features while enhanced extreme learning machines are employed for better classification with less computational complexity. The different data sources are used to collect the EEG signals in which the collected signals are preprocessed for evaluating the proposed model. Additionally, the experiments are performed by DEAP and Kaggle datasets as well as performance parameters and compared by conventional Fused Support vector machines (F-SVM),BI-Long Short Term Memory(BILSTM), Random Forest(RF) and Deep Convolutional Neural network(DCNN). Results shows which proposed EEG based SDS has better performance than other conventional ones of high accuracy in stress detection and for diagnosing classify the stress-levels .
Unlocking Machine Learning Model Decisions: A Comparative Analysis of LIME and SHAP for Enhanced Interpretability
Authors: Sarvesh Koli Komal Bhat Prajwal Korade Deepak Mane  Anand Magar Om Khode 
Published: April 2024

XAI is critical for establishing trust and enabling the appropriate development of machine learning models. By offering transparency into how these models make judgements, XAI enables researchers and users to uncover potential biases, admit limits, and eventually enhance the fairness and dependability of AI systems. In this paper, we demonstrates two techniques, LIME and SHAP, used to improve the interpretability of machine learning models. Assessing Explainable AI (XAI) approaches is critical in searching for transparent and interpretable artificial intelligence (AI) models. Explainable AI (XAI) approaches are designed to provide insight into how complex models make decisions. This paper thoroughly analyzes two prominent XAI methods: Shapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). This study aims to understand the decision made by a machine learning model and how the model came to that decision. We discuss the approaches and framework of both LIME and SHAP and assess their behavior in predicting the model's outcome.
Building A Smart Campus: Designing An Architectural Framework For Campus Security Gates And Vehicle Asset Management With GPS Tracking Technology
Authors: Thelma D. Palaoag Reina T. Payongayong, 
Published: April 2024

Modern technology must be integrated into educational institutions as they develop into "smart campuses" in order to guarantee the security and effectiveness of day-to-day operations. This study combines GPS tracking technology to present an architectural framework for the deployment of intelligent security gates and automotive asset management systems. The objective is to build a secure and effective environment that improves campus security and maximizes the use of available resources. The suggested architecture combines a centralized software platform with innovative hardware elements, including GPS trackers, RFID readers, and security cameras. RFID-enabled security gates improve access control by limiting entrance to the college grounds to only authorized people and vehicles. Surveillance cameras simultaneously offer real-time monitoring to guarantee adherence to security procedures.
The vehicle asset management system utilizes GPS technology to monitor campus vehicles in real-time, increasing fleet management efficiency and enhancing general campus security. GPS tracking integration allows scheduling maintenance, optimizing routes, and notifying authorities of unwanted vehicle movements.
Improving Task Scheduling in Cloud Datacenters by Implementation of an Intelligent Scheduling Algorithm
Authors: Bassim M. Salih Omer K. Jasim Mohammad, 
Published: April 2024

The need for mobile and online Apps and services has led to rapid growth in cloud computing services. The rapid increase in growth highlights the importance of reducing the time required for scheduling and efficiently utilizing resources in a dynamic setting. Consequently, numerous scheduling algorithms have been devised to address these problems by employing intelligent scheduling approaches, including Genetic Algorithms, greedy algorithm, Antlion Optimizer, Ant Colony optimization, and Cuckoo Intelligent Algorithm. This study provides an overview of intelligent optimization strategies, with a specific focus on the Cuckoo intelligent methodology. Additionally, it presents a proposed implementation of a Cuckoo-based cloud computing environment as an efficient algorithm that is projected to yield improved results in task scheduling.
Optimized Deep Learning for Human age Estimation
Authors: R. P. Bhavsar, B. V. Pawar Kirti A. Patil, 
Published: April 2024

Background/ Introduction: Human age assessment plays a crucial part in diagnosing genetic problems, and development abnormalities in children. They are also used in several applications, like forensic investigation and criminal scenes. Generally, human age is estimated from X-ray images of bones, and the manual estimation of human age is highly subjective, as they depend on the medical experience of the professionals. Further, they are prone to error and are laborious, thereby requiring automated Bone Age Assessment (BAA) for determining human age with high accurateness.
Materials and methods: This work presents a novel approach using Deep Learning (DL) and optimization considering the X-ray images of hand. Here, human age is estimated using the Deep Residual Network (DRN), whose parameters are trained using the proposed Beluga whale lion optimization (BWLO) algorithm. Further, several processes, like pre-processing, Region of Interest (RoI) extraction, Image augmentation, and feature extraction are used to enhance the accuracy of the estimation model.
Results and conclusion: The BWLO_DRN is examined for its superiority considering metrics, like accuracy, Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV).
The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study
Authors: Sonali Kadam Vaishali Mishra, 
Published: April 2024

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.
The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.
The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.
In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.
Analysing Trends in Trading Patterns in Financial Markets Using Deep Learning Algorithms
Authors: Yanke Huang Xia Zhao, 
Published: April 2024

For many years, academics from various fields have been fascinated by the topic of financial market forecasting. Using machine learning techniques like support vector machines (SVM) and reinforcement learning, several studies have been conducted to anticipate movements in the stock market. The whole body of research on Trading Patterns prediction or trading that used reinforcement learning as their primary machine learning method was thoroughly reviewed. Unreasonable assumptions were made in every article that was evaluated. Therefore, employing a weighted sum unit, the research aims to give a combined Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The models' performance is maximised on a testing dataset by obtaining and combining predictions from both models with precisely chosen weights. The proposed method aims to capitalize on the complementary strengths of both models, mitigating their individual limitations. The Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and direction accuracy validate the efficacy
Psr Model with Intelligent Min-Max Estimation Deep Learning (Imin-Maxedl) for Public Service Evaluation of Sports Venues
Authors: Li Deqi 
Published: April 2024

Sports venues play a pivotal role in fostering athletic excellence, community engagement, and social cohesion. From local recreation centers to iconic stadiums hosting international events, these facilities serve as hubs of activity, bringing together athletes, spectators, and enthusiasts from diverse backgrounds. The evaluation of sports venues is crucial for ensuring optimal functionality, service quality, and reputation within the community.
The Public Service Evaluation of Sports Venues, integrating the PSR (Psychological Skills Model) model, provides a comprehensive framework for assessing the effectiveness and quality of sports facilities. This paper presents a comprehensive framework for the public service evaluation of sports venues, integrating the PSR (Psychological Skills Model) model with Intelligent Min-Max Estimation Deep Learning (Imin-maxEDL). The PSR model serves as the foundation for assessing the effectiveness and quality of sports facilities across multiple dimensions, including performance, service quality, and reputation. Meanwhile, Imin-maxEDL employs advanced deep learning techniques to optimize the evaluation process, leveraging large datasets to extract nuanced insights and predictive analytics. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, demonstrating significant improvements in accuracy, efficiency, and predictive capabilities compared to traditional evaluation methods. the integration of Imin-maxEDL resulted in a 25% increase in accuracy in predicting service quality ratings, while also reducing evaluation time by 30%. Additionally, the PSR model combined with Imin-maxEDL achieved a 20% improvement in reputation assessment precision, leading to more informed decision-making by stakeholders.
Equatorial Plasma Bubbles Characteristics Studies using IRNSS Data around Bangalore
Authors: Raju G. Chaluvaraju P. P., 
Published: April 2024

TEC analysis stands as a cornerstone in navigation, enabling the examination of sudden shifts in Total Electron Content (TEC). Equatorial Plasma Bubbles (EPBs) are discerned through TEC analysis, which proves superior to alternative approaches. This method furnishes insights into EPB occurrences, offering valuable data on their daily and seasonal fluctuations within the ionosphere. Analysis of TEC data from January 2015 to 2022, derived from IRNSS observations in Bangalore, reveals patterns in EPB occurrence correlated with the sunspot cycle and solar flux variations. This alignment, observed during a phase of low solar activity, underscores the reliability of TEC analysis in capturing ionospheric dynamics.
Enhancing Industrial IoT Security: Utilizing Blockchain-Assisted Deep Federated Learning for Collaborative Intrusion Detection
Authors: Jegatheesan A. Anitha Govindaram, 
Published: April 2024

The Industrial Internet of Things (IIoT) is a rapidly evolving features with multiple applications, including critical infrastructure. Privacy policies are required to preserve the protection of user data in the threat intelligence community. Blockchain is a modern technology which used recently to provide more secure storage and efficiency. In this research, Blockchain Assisted Deep Federated Learning (BC_DFL) system is used to detect intruders. The three key processes used in the proposed intrusion detection architecture are data collection, pre-processing and intrusion detection. Data normalization, reduction, cleaning and transformation are used in pre-processing to remove extraneous information and improve data quality. This pre-processed data is sent to the Blockchain Assisted Deep Federated Learning (BC_DFL) system for intrusion detection. To detect intruders, the federated learning-based Capsule Auto-Encoder (FL_CAE) architecture first learns the properties from the inputs. Blockchain technology (BCTech) is not only used for storage but also improves security by eliminating the possibility of threatening node and individual server failure. The ToN_IoT and UNSW-NB15 data sets are used in the implementation and performance evaluation study. The proposed model is evaluated using existing in the Results section. In the UNSW-NB15 dataset, proposed model achieved an accuracy, precision, recall and F1 score of 97.26%, 97.28%, 96.89% and 96.96% respectively as well as in ToN_IoT data set proposed model achieved an accuracy, precision, recall and F1 score of 95.74%, 99.54 %, 99.49% and 99.24%, respectively. The execution of the proposed approach takes 2.31 seconds in the UNSW-NB15 dataset and 1.66 seconds in the ToN_IoT dataset. Blockchain offers a transparent and impenetrable ledger for transaction recording and verification. Within the framework of cooperative intrusion detection, it guarantees a secure and reliable exchange of threat intelligence and detection models between various users of IIoT ecosystem.
Customized Mechanism for Diabetic Risk Prediction: A Hybrid CNN–Autoencoder Approach with Emphasis on Retinal Imaging in the Elderly
Authors: Drakshayani Desai Harsha Jitendra Sarode 
Published: April 2024

Diabetes Mellitus presents a substantial health obstacle on a global scale, with a particular impact on the elderly demographic. Prompt identification is vital for efficient control and avoidance of complications. This study introduces a new Hybrid Convolutional Neural Network (CNN) and Autoencoder model specifically developed for accurately predicting the risk of diabetes at an early stage. The model is specifically designed to analyze retinal images in older individuals. The introduction of this paper presents a comprehensive analysis of the increasing incidence of diabetes in the elderly population and underscores the significance of early identification. Conventional approaches frequently encounter constraints in terms of precision and specificity, which has led to the investigation of sophisticated machine learning models. The Hybrid CNN–Autoencoder model combines the advantageous characteristics of both architectures, utilizing the CNN proficiency in extracting spatial features and the Autoencoder's capability for unsupervised feature learning. The approach we use consists of training and validating the model using a comprehensive dataset of retinal images from elderly individuals. The model attains a remarkable accuracy of 90.92%, outperforming the typical deep learning and machine learning models frequently employed in predicting diabetic risk. The experimental results demonstrate the superior performance of the Hybrid CNN–Autoencoder model in terms of accuracy, sensitivity, and specificity. Comparative analysis shows that it is highly effective in identifying subtle patterns that indicate early signs of diabetes, surpassing traditional models and other modern deep learning methods. The research findings presented in this study make a valuable contribution to the expanding knowledge base on diabetes detection, specifically within the elderly population. The proven precision of the suggested model highlights its capacity as a dependable and tailored instrument for early forecasting, thus enabling prompt interventions and individualized healthcare strategies for individuals susceptible to diabetes.