bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Development of Digital Economy in Shanxi Province in the Era of Artificial Intelligence
Authors: Xueqing Sun Jiangxin He,
Published: April 2024

With the development of a new round of scientific and technological revolution and industrial transformation, new information technologies such as 5G technology, big data and artificial intelligence have become an important driving force to promote the development of digital economy. In the context of the era of artificial intelligence, Shanxi Province, as a relatively latecomer in the economic development of domestic provinces, should seize the opportunity to keep up with the trend of The Times. Through the analysis of some data of Shanxi's digital economy in recent years and the comparison with other provinces, it is found that Shanxi has some problems in digital infrastructure, industrial digitization, digital industrialization and digital government construction. With artificial intelligence as the background, through investigation, literature and other methods to accelerate the construction of digital infrastructure, expand the scale of digital industrialization, implement industrial digital transformation projects, and strengthen the construction of digital government as basic measures, Shanxi can realize the rapid development of digital economy. These measures are of great significance for Shanxi Province to formulate corresponding measures from the perspective of problems in the process of digital economy development, so as to achieve industrial transformation and high-quality economic development
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
Recurrent Chain Feedforward Bayesian Statistic Network (Rcf-Bsn) Model: A Fuzzy Based Approach to Recognize Entrepreneurship and the Imperative for Education to Adapt
Authors: Fanpeng Meng, Dongquan Han Dan Li, Shenzhe Zhou,
Published: April 2024

Entrepreneurial methods and ability enhancement are crucial aspects of college education, empowering students to develop the skills and mindset necessary for success in the dynamic world of entrepreneurship. Through targeted initiatives and experiential learning opportunities, colleges strive to foster innovation, creativity, and entrepreneurial spirit among their students. These programs often include courses on business fundamentals, startup management, and market analysis, complemented by hands-on projects, mentorship programs, and networking events. This paper proposed innovative entrepreneurial methods and the enhancement of ability among college students in the new era, employing Bayesian statistics with the Recurrent Chain Feedforward Bayesian Statistic Network (RCF-BSN). Recognizing the evolving landscape of entrepreneurship and the imperative for education to adapt accordingly, this research aims to explore effective methodologies for fostering entrepreneurial skills and mindset among college students. Results from the study shed light on the effectiveness of different entrepreneurial methods, such as experiential learning, mentorship programs, and startup incubators, in fostering entrepreneurial abilities among college students. Additionally, the study identifies key factors contributing to entrepreneurial success in the new era, including adaptability, resilience, and creativity. Quantitative analysis using Bayesian statistics reveals that students who participated in experiential learning programs showed a 30% increase in entrepreneurial self-efficacy scores compared to those who did not participate. Additionally, students who received mentorship through startup incubators demonstrated a 25% higher likelihood of launching their own ventures within one year of graduation.
Geometric Co-Ordinate Feature Point Estimation (Gfpe): A Novel Approach to Analyse Of Kinematic Characteristics of Yoga Asanas Based on Anybody Simulation Software
Authors: Guan Jiao
Published: April 2024

The kinematic characteristics of yoga asanas is crucial for comprehending the biomechanical demands and benefits associated with these postures. Utilizing AnyBody simulation software, researchers delve into the intricate movements and alignments involved in various yoga poses. Through advanced biomechanical modeling, AnyBody allows for the analysis of joint angles, muscle activations, and forces exerted on the body during asana practice. This paper presented an analysis of the kinematic characteristics of yoga asanas using AnyBody Simulation in conjunction with Geometric Co-ordinate Feature Point Estimation (GFPE). By integrating advanced biomechanical modelling capabilities of AnyBody with GFPE’s precision in identifying geometric features, this study aims to elucidate the intricate movements and alignments inherent in various yoga poses. Through comprehensive simulations, joint angles, muscle activations, and forces exerted on the body during asana practice are meticulously analyzed. The examination of body posture with GFPE in the downward-facing dog pose, the average joint angles of the shoulder, elbow, and wrist were found to be 130°, 150°, and 160°, respectively. Additionally, muscle activation levels were measured, with the triceps brachii exhibiting an average activation of 70% of maximum voluntary contraction during the pose.
Stacked Ranking Feature Cluster Machine Learning (Srfcml): A Novel Method of Career Planning of College Students Based on Career Interest Assessment and Machine Learning
Authors: Lv Jing
Published: April 2024

Career interest assessment, powered by machine learning algorithms, revolutionizes the way individuals explore and align with career paths. By analyzing vast datasets encompassing factors such as skills, preferences, personality traits, and job market trends, machine learning models can provide personalized career recommendations tailored to individual strengths and aspirations. These algorithms leverage advanced techniques such as natural language processing (NLP) to interpret self-assessment responses and match them with suitable career options. Additionally, machine learning algorithms continuously refine their recommendations based on user feedback and real-world outcomes, ensuring accuracy and relevance over time. This paper presents a novel approach to career planning for college students, integrating career interest assessment with machine learning techniques, specifically utilizing the Stacked Ranking Feature Cluster Machine Learning (SRFcML) model. The proposed framework leverages large-scale datasets encompassing diverse factors such as academic performance, skills, interests, and industry trends to provide personalized career recommendations. Through the application of machine learning algorithms, including clustering and ranking techniques, the SRFcML model identifies relevant clusters of career options and ranks them based on individual preferences and aptitudes. This approach enables college students to explore and prioritize career paths aligned with their unique strengths and aspirations. Simualtion results demonstrated that the effectiveness of the proposed framework was evaluated, yielding promising numerical results. For instance, based on self-assessment responses and academic performance data, the SRFcML model achieved an average accuracy of 85% in recommending suitable career paths for college students. Furthermore, in a comparison with traditional career planning methods, the proposed framework demonstrated a 30% improvement in the alignment between recommended career options and students' preferences. Additionally, user satisfaction surveys revealed a high level of confidence and trust in the recommendations provided by the SRFcML model, with 90% of participants expressing satisfaction with the accuracy and relevance of the career suggestions.
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.
Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation
Authors: Wang Jun
Published: April 2024

In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.
Virtual Reality Technology in Practical Teaching Methods
Authors: Yinhui Hao Fuqiang Qiu,
Published: April 2024

This paper explores the integration of virtual reality (VR) technology into practical teaching methods, enhanced by the Whale Optimized Feature Extraction Recurrent Classification (WOFERC) model. The study aims to enhance the effectiveness and engagement of practical learning experiences through immersive VR simulations. Using simulated experiments and empirical validations, the efficacy of the WOFERC-enhanced VR teaching approach is evaluated. Results demonstrate significant improvements in student performance and engagement compared to traditional methods. For example, students using the WOFERC-enhanced VR platform achieved an average score increase of 20% in practical assessments. Additionally, the framework allowed instructors to optimize teaching strategies based on real-time student feedback, leading to more effective and personalized instruction. These findings highlight the potential of VR technology with WOFERC in transforming practical teaching methods and fostering immersive and impactful learning experiences.
Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market
Authors: Leilei Dong Chao Yang,
Published: April 2024

An intelligent talent recommendation algorithm is a sophisticated system that utilizes advanced data analysis techniques and machine learning algorithms to match individuals with suitable job opportunities or educational programs. By analyzing a wide range of data points such as skills, qualifications, work experience, and preferences, these algorithms can generate personalized recommendations tailored to each individual's unique profile and career goals. Moreover, intelligent talent recommendation algorithms continuously learn and improve over time, refining their recommendations based on user feedback and performance data. This paper presents an intelligent talent recommendation algorithm tailored for college students preparing for the future job market, utilizing Ranking Hidden Chain Deep Learning (RHC-DL). The algorithm aims to provide personalized recommendations by analysing students' skills, qualifications, interests, and career aspirations. Through simulated experiments and empirical validations, the efficacy of the RHC-DL-enhanced recommendation algorithm is evaluated. Results demonstrate significant improvements in recommendation accuracy and relevance compared to traditional methods. For instance, students using the RHC-DL algorithm reported a 40% increase in job offer acceptance rates and a 30% improvement in job satisfaction levels. Additionally, the algorithm adapts and learns from user interactions, continuously refining its recommendations based on real-time feedback.
Optimization of Resource Allocation of University Innovation and Entrepreneurship Education Based on Collaborative Filtering Algorithm
Authors: Zheng Junjun
Published: April 2024

Entrepreneurship education resource allocation involves the strategic distribution of resources to support programs and initiatives aimed at fostering entrepreneurial skills and mindset among students. These resources can include funding, faculty support, curriculum development, mentorship opportunities, and access to networks and facilities. Effective resource allocation ensures that entrepreneurship education programs are adequately equipped to provide students with the knowledge, skills, and support needed to succeed as entrepreneurs. By prioritizing resource allocation to areas such as experiential learning, incubation spaces, and networking events, institutions can create a vibrant ecosystem that nurtures innovation and encourages entrepreneurial ventures. This paper presents an innovative approach to optimizing the resource allocation of university innovation and entrepreneurship education through the application of a collaborative filtering algorithm, enhanced by Flemingo Optimized Collaborative Filtering Classification (FOCFC). The study aims to address the challenge of efficiently allocating resources such as funding, mentorship, and infrastructure to support innovation and entrepreneurship initiatives within universities. Through simulated experiments and empirical validations, the effectiveness of the FOCFC-enhanced collaborative filtering algorithm is evaluated in recommending resource allocations tailored to the unique needs and preferences of students and entrepreneurial ventures. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods, with the FOCFC model achieving a precision rate of 95% in recommending resource allocations. Additionally, the model provides valuable insights into emerging trends and opportunities in the innovation and entrepreneurship ecosystem, enabling universities to adapt their resource allocation strategies proactively. These findings highlight the potential of collaborative filtering algorithms with FOCFC in optimizing resource allocation for university innovation and entrepreneurship education, fostering a supportive and conducive environment for entrepreneurial success.
Evaluation of Innovation and Entrepreneurship Culture Atmosphere in Universities Based on Sentiment Analysis
Authors: Wu Yuanyuan
Published: April 2024

The entrepreneurship culture atmosphere in universities plays a pivotal role in shaping the mindset and behavior of students towards innovation and risk-taking. Through sentiment analysis, universities can gauge the prevailing attitudes and emotions towards entrepreneurship among students and faculty members. A positive entrepreneurship culture atmosphere fosters an environment of creativity, resilience, and collaboration, encouraging students to pursue entrepreneurial ventures and take calculated risks. By promoting a supportive ecosystem that celebrates failure as a learning opportunity and provides resources and mentorship for budding entrepreneurs, universities can cultivate an entrepreneurial spirit among their community members. Moreover, sentiment analysis enables universities to identify areas for improvement and tailor interventions to enhance the entrepreneurship culture atmosphere further. This paper presents an evaluation of the innovation and entrepreneurship culture atmosphere in universities using sentiment analysis, enhanced by Automated Recommender Probabilistic bi-gram Sentimental Analysis (ARPbi-gSA). The research aims to assess the prevailing sentiments and attitudes towards innovation and entrepreneurship among students and faculty members within university communities. Through the analysis of textual data from various sources such as social media posts, surveys, and academic literature, the ARPbi-gSA algorithm evaluates the sentiment expressed towards entrepreneurship initiatives, programs, and events. Results from sentiment analysis provide insights into the overall positivity or negativity surrounding the entrepreneurship culture atmosphere, identifying strengths and areas for improvement. The sentiment analysis conducted using ARPbi-gSA revealed a sentiment score of 0.75, indicating a predominantly positive sentiment towards entrepreneurship initiatives within the university. Out of 1000 social media posts analyzed, 650 expressed positive sentiments, while 250 were neutral, and 100 were negative, reflecting an overall positive sentiment towards entrepreneurship culture. Based on the sentiment analysis findings, the ARPbi-gSA algorithm provided recommendations for enhancing the entrepreneurship culture atmosphere, resulting in a 15% increase in student participation in entrepreneurial activities over the academic year.
Design of Visual Communication Experience Based on Virtual Reality Technology
Authors: Guangkai Dai Qi Yang, Shulin Hua,
Published: April 2024

Visual communication experiences have been revolutionized by virtual reality (VR) technology, offering users immersive and captivating interactions with digital content. Through VR headsets and immersive environments, users can engage with visual content in unprecedented ways, feeling as though they are physically present within the digital space. Whether exploring virtual art galleries, immersive storytelling experiences, or interactive visualizations, VR technology provides users with a sense of presence and agency, enhancing their connection to the content. Furthermore, VR enables creators to design highly engaging and interactive visual experiences that transcend traditional media formats, allowing for innovative storytelling techniques and immersive narratives. This paper presents the design of a novel visual communication experience leveraging virtual reality (VR) technology enhanced by Multi-Spectral Multi-Point Co-Ordination (MSMPC). The proposed design aims to create immersive and engaging visual experiences that transcend traditional media formats. Through simulated experiments and empirical validations, the effectiveness of the MSMPC-enhanced VR-based visual communication experience is evaluated. Results demonstrate significant improvements in user engagement, immersion, and satisfaction compared to conventional approaches. The users interacting with the MSMPC-enhanced VR experience reported a 40% increase in immersion levels and a 25% improvement in overall satisfaction ratings. Additionally, the design enabled creators to optimize visual storytelling techniques based on real-time user feedback and performance data, leading to more impactful and effective communication. These findings highlight the potential of MSMPC in enhancing visual communication experiences based on VR technology, paving the way for innovative and immersive storytelling in various domains.
Microaneurysms and Exudates Detection in Retinal Images using Deep Neural Network
Authors: S. R. Ganorkar P. N. Maldhure,
Published: April 2024

One frequent diabetes consequence that affects the eyes is diabetic retinopathy(DR). The most frequent reasonfor blindness in working-age adults in the world is diabetic retinopathy. One in three diabetics has diabetic retinopathy to some extent. DR affects nearly all persons having type 1 diabetes and more than 60% of people having type 2 diabetes to some extent after 20 years of diabetes. In the US, approximately 4.2 million persons, 40 and older have diabetic retinopathy. In the United States, 12% of all new occurrences of blindness are caused by diabetic retinopathy.A 95% reduction in the risk of blindness is possible with a diabetic retinopathy early detection and treatment. The retina's appearance, the presence or absence of Microaneurysm and Exudates, and the degree of participation are all taken into account in the grading process. It has been demonstrated that deep neural networks (DNNs) work well for automatically grading diabetic retinopathy. The features like Microaneurysm and Exudates that are diagnostic of various stages of diabetic retinopathy are taught to these DNNs utilizing vast datasets of retinal pictures and accompanying grading information.It has been demonstrated that deep neural networks (DNNs) are efficient in automatically detecting diabetic retinopathy from retinal pictures. Sensitivity, specificity, precision, accuracy, and Kappa value are used to measure how well DNNs work in detecting diabetic retinopathy; these values are 95.74%, 92.31%, 96.77%, 94.74%, and 0.87, respectively.
UOWC Integration with ROVs: A Fiber-Optic Approach for Enhanced Underwater Exploration
Authors: Dipak P. Patil Swati Gandhi,
Published: April 2024

Underwater Optical Wireless Communication (UOWC) revolutionizes underwater exploration, leveraging seawater's unique property—reduced absorption of blue-green light. This offers superior advantages over traditional communication methods, providing higher bandwidth and lower latency. This paper explores the integration of UOWC with Remotely Operated Vehicles (ROVs), enhancing data exchange and command capabilities in challenging underwater environments. Seawater's unique properties empower UOWC to outperform acoustic and RF methods, crucial for real-time applications. The study utilizes advanced techniques like Fiber-Optic Communication, High Order Sliding Mode Control, and Hybrid Blockage Detection for ROV integration and control. While the abstract focuses on theory and design, it sets the stage for empirical validations, emphasizing the need for future research to quantify improvements in real-world underwater scenarios
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.
DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans
Authors: Lata L. Ragha Seema B. Rathod,
Published: April 2024

Lung cancer is a critical global health concern, necessitating precise early diagnosis and intervention for better patient outcomes. Computed Tomography (CT) scans are pivotal in lung cancer detection, and leveraging advanced technology is crucial. This study introduces "DLCTLungDetectNet," a Convolutional Neural Network (CNN) based deep learning framework, with a focus on early lung cancer detection using CT scan images. The core innovation lies in the integration of the robust "FusionNet," a hybrid model amalgamating feature from ResNet50 and InceptionV3. We conduct a comprehensive comparative analysis, showcasing the superior performance of DLCTLungDetectNet over established architectures such as VGG16, VGG19, and Inception v3. Rigorous evaluation based on standard metrics substantiates DLCTLungDetectNet's high accuracy, precision, Area Under Curve (AUC), and F1 score. This research not only highlights the potential of deep learning in enhancing lung cancer diagnosis but also establishes a benchmark, showcasing the efficacy of the FusionNet hybrid model for achieving superior accuracy in automated lung tumor detection.
Analysis the Impact of Distributed Generation Interconnection on the 20 kV Distribution Network: A Study Case of the Waste-to-Energy in Bantar Gebang and the Tambun Area Distribution Network
Authors: Wasimudin Surya Saputra, Agus Heri Setya Budi Tasma Sucita, Muhammad Feby Nurrahman Rosandi,
Published: April 2024

The need for electricity in Indonesia is increasing along with the growth of world industry in Indonesia. The use of power plants with fossil energy sources is decreasing with the aim of improving the environmental system. Electricity needs can be fulfilled by Distributed Generation (DG), namely generation that is connected to a distribution network in order to fulfill power needs and improve electricity quality. In Bekasi City, the Directorate General of the Bantar Gebang Waste-to-Energy Plant (WtE) is being built with a drained power of around 786 kW. In this research, an analysis will be carried out on the Bantar Gebang WtE, which is connected to the Tambun area distribution network. The simulation results show that the voltage drop at the transformer load is 0.3% to 3% in general. With PVUR values on sample transformers of 0.171%, 0.578%, and 0.187% and real and reactive power losses on Underground Lines, Overhead Lines, and transformers of 144 kW and 173 kvar leading before interconnection. After interconnection, there was an increase in real power loss to 147 kW and an improvement in PVUR values to 0.156%, 0.563%, and 0.183%, and reactive power to 164 kvar. The use of power at the Tambun feeder Dodge Substation has also decreased due to the interconnection of the Bantar Gebang WtE, which can generate around 786 kW of power to supply power to the distribution network connected to the Bantar Gebang WtE DG.
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.
BiLSTMs and BPE for English to Telugu CLIR
Authors: K. V. V. Satyanarayana, M. S. V. S. Bhadri Raju B. N. V. Narasimha Raju,
Published: April 2024

A crucial component of Cross Lingual Information Retrieval (CLIR) is Neural Machine Translation (NMT). NMT performs a good job of transforming queries in the English language into Indian languages. This study focuses on the translation of English queries into Telugu. For translations, the NMT will make use of a parallel corpus. Due to a lack of resources in the Telugu language, it is exceedingly challenging to provide NMT with sizable parallel corpora. Thus, the NMT will encounter an issue known as Out of Vocabulary (OOV). Long Short-Term Memory (LSTM) with Byte Pair Encoding (BPE), which breaks up rare words into subwords and attempts to translate them to solve the OOV issue. Issues such as Named Entity Recognition (NER) continue to plague it. In sequence-to-sequence models, bidirectional LSTMs can solve certain NER challenges. Systems that need to be trained in both directions to recognize named entities can benefit from the use of Bidirectional LSTMs (BiLSTMs). The translation efficiency of NMT with BiLSTMs is significantly higher than normal LSTMs, as indicated by the accuracy metrics and Bilingual Evaluation Understudy (BLEU) score.
Design a Deep Learning Model for Dairy Cow Abnormal Behaviour Prediction
Authors: Sanjay Kumar Sharma Jayesh Surana,
Published: April 2024

When it comes to determining the health and well-being of cattle, it is critical to observe their behaviour. Another strong and cost-effective monitoring approach is a neural network-based monitoring system that analyses time series data from inertial sensors attached to cows. This is one of the most powerful and cost-effective monitoring systems currently available. Even though deep learning has made significant advances in pattern detection, large datasets are still required in many cases. When only a little amount of data is available, data augmentation is a very effective and low-cost pre-processing step for neural network-based systems. To study and analysis, a variety of ways for enhancing inertial sensor data based on cow behavioural data are discussed. Using convolutional neural networks, researchers are attempting to solve the difficult problem of identifying cow behaviour, which is made much more difficult by a scarcity of training data. Our proposed approaches areimproving the effectiveness of deep learning (CNN+LSTM) in the classification of cow behaviour while also lowering the total system costs associated with data collection and labelling.
Secure Image Encryption Techniques with Fuzzy based Operation Modes for Trustworthy Accessing in Communication Devices
Authors: Y. Rama Mohan, Chatakunta Praveen Kumar Morarjee Kolla, K. Rajendra Prasad, K. Sreenivasulu,
Published: April 2024

Digital networks present a number of interesting research problems, one of which is the secure image transfer through various channels of communication. In these circumstances, cryptography algorithms are commonly employed to securely encrypt and decode data at both the sending and receiving ends. To determine the most effective method of image encryption, a wide range of modern algorithms are analysed and compared. In this study, we describe several fuzzy modes of operation for image encryption. It guarantees the safety of digital image transfer. The work's goal is to provide the most trustworthy image encryption possible; This is achieved by integrating image encryption methods into a hybrid fuzzy based architecture. The proposed composite fuzzy-based encrypted systems (CFES) are recommended for use in order to ensure the security of data on various communications media. The composite fuzzy-based encrypted systems provide strong security while yet protecting users' privacy while viewing image content.
Hue Channel Mesoscale Convective System Identification Techniques using Kalpana Geostationary Satellite Observations
Authors: Anuradha Phadke ,Subrata Kumar Das Vidya Patil ,
Published: April 2024

Climate change is leading to sudden extreme weather events like floods and heavy rain to occur. One of the most significant rain-bearing systems, Mesoscale Convective Systems (MCSs), is in charge of catastrophic rainfall and flood events that may cause loss of life and property. MCSs are one of the vital components of the climate system on Earth. Many studies contributed to two significant steps, MCS identification and tracking. There are a variety of algorithms focused on tracking whereas for MCS identification only limited methods are present. They contribute to global as well as regional climate patterns by transporting heat and moisture. It is necessary to identify MCS properly to track MCS occurrences over time and comprehend their typical lifecycle to get early warnings of their existence. Three MCS identification techniques based on the Hue channel of the Hue Saturation Value (HSV) color model are implemented in this research, and their effectiveness is assessed. These techniques execute segmentation based on Hue channel Thresholding (HT), K means clustering combined with Hue channel Thresholding (KMCHT) and the modified Source Apportionment Technique combined with Hue channel Thresholding (SATHT). Image pixel values are used to depict infrared brightness temperature data obtained from the Indian geostationary satellite Kalpana-1. The generated ground truth images and performance measurements are used to assess the effectiveness of the methods. The proposed SATHT method for multiple cloud segmentation results in superior performance metrics than the HT and KMCHT approaches.
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.
Privacy-Preserving and Ranked Search Using Advanced Multi-Keyword Scheme Over the Encrypted Cloud Environment
Authors: Kuldeep P. Sambrekar Narendra Shyam Joshi ,
Published: April 2024

In order to enable users to extract useful insights on demand, this paper presents a unique approach for secure and privacy-preserving ranked search in cloud environments. There is a rising need for strong systems that strike a balance between data utility and individual privacy as cloud-based data processing and storage become more and more prevalent. Our suggested methodology makes use of sophisticated multi-keyword operators and encryption methods to offer a safe and effective solution for cloud-based ranked search that protects privacy. Our methodology's essential component is the combination of a safe encryption technique and sophisticated multi-keyword search operators. With this combination, users can do ranked searches on encrypted data while maintaining the privacy of the underlying data. Our approach leverages strong cryptography protocols and an advanced indexing mechanism to provide rapid retrieval of relevant results while protecting confidential information from unauthorized individuals. Secure query processing techniques and encryption are added for added privacy protection. This guarantees that throughout the ranking search operations, the decrypted data cannot be accessed by the cloud service provider or any other parties participating in the data processing pipeline. Users don't have to compromise the privacy of their stored data or expose the exact text of their queries to obtain ranked results based on several keywords. Our technology is especially useful in situations when customers need instantaneous insights from data stored in the cloud without compromising privacy. Our technology protects the secrecy of the underlying datasets while enabling customers to obtain secure insights on demand in a variety of sensitive sectors, including financial analysis and medical research. The suggested method offers a strong and adaptable way to achieve privacy-preserving ranked search in cloud environments. Through the incorporation of sophisticated multi-keyword operators and encryption strategies, our approach guarantees users may effectively obtain pertinent data while maintaining personal privacy. With its ability to provide safe, anytime access to insights stored in cloud-based data repositories, this breakthrough has enormous potential for a variety of applications.
PMSIEMDL: Design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning Fusions
Authors: Sarika Khandelwal Ashwini Raipure,
Published: April 2024

Identification of student inclination towards different educational fields requires integration of deep pattern learning models with temporal data analysis techniques. These techniques are highly context sensitive, and cannot be scaled for analysis of students that have an interest in multiple domains. Moreover, existing deep learning models are highly complex, and showcase moderate performance when used on real-time datasets. To overcome these limitations, this text proposes design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning fusions. The proposed model initially collects data samples from a large number of students, and segregates them into different classes. These include social data, personal habits data, education data, family related data, performance data and future inspiration data classes. These datasets were combined with a customized psychological questionnaire which was curated by experts in the field of student counselling & psychology. Based on student responses, their entity specific classes were generated, that were separately trained via different Convolutional Neural Network (CNN) Models, which assists in identification of student-performance at individual-class levels. These performances are compared with existing inclination datasets via a fusion of Long-Short-Term Memory (LSTM) & Gated Recurrent Neural Network (GRNN), which assists in identification of correlation between subject-level inclinations & their entity classes. This provides with a probabilistic map of different subjects towards which the student might be inclined, and assists them to select their study streams. The generated map was validated for multiple students, and recommendations were made based on higher probability values, which assisted in identification of student inclination levels. The model was evaluated under large datasets and its performance was compared with various state-of-the-art methods under different scenarios. Based on this comparison, it was observed that proposed model was capable of achieving 8.5% better recommendation accuracy, 4.9% higher prediction precision, 6.5% better recommendation recall & 2.9% better Area Under the Curve (AUC) levels, which makes it highly useful for a wide variety of student inclination use cases
An Experimental Study Using Deep Neural Networks to Predict the Recurrence Risk of Brain Tumor Glioblastoma Multiform
Authors: Chetan J. Shelke Disha Sushant Wankhede,
Published: April 2024

Virtual reality (VR) technology within the hotel industry marks a transformative shift in the way guests experience and engage with hospitality services. Virtual reality, with its immersive and interactive capabilities, enables hotels to provide a novel and engaging environment for guests. From virtual tours of hotel rooms and amenities to immersive experiences showcasing local attractions and cultural highlights, VR has the potential to revolutionize the pre-booking and on-site guest experience. This paper focused on the user experiences within hotel rooms enhanced with virtual reality (VR) technology. Leveraging content analysis, sentiment analysis, and advanced classification models, we aim to unravel the intricacies of user sentiments and preferences in this evolving domain. The content analysis reveals a spectrum of user opinions, ranging from enthusiastic endorsements of immersive VR content to nuanced critiques of room ambiance and interactivity. Subsequently, a sentiment analysis model accurately categorizes these sentiments, showcasing its effectiveness in capturing the diverse user expressions. Our classification analysis demonstrates the robustness of the sentiment analysis model, with high accuracy, precision, recall, and F1-score metrics. Comparatively, we introduce a proposed BERT model, harnessing advanced natural language processing techniques, and observe its performance against traditional sentiment analysis and an AutoEncoder Model. The results indicate that the BERT model matches the performance of traditional sentiment analysis, outperforming the AutoEncoder Model. This underscores the effectiveness of leveraging state-of-the-art language models in understanding and classifying user sentiments.
Hres Integrated Dstatcom Using High Gain Sepic-Zeta Based Woa Optimized Anfis-Mppt Controller for Pq Issues
Authors: 2Dr. V. Jayalakshmi R. Sathyapriya ,
Published: April 2024

The power generated by renewable energy sources (RES), including photovoltaic (PV) and wind energy systems, are a great deal dependent on climate situations that result in Power Quality (PQ) issues, which require rapid adjustment of energy transmission and distribution systems. As a solution, (Distribution Static Synchronous Compensator) DSTATCOM is implemented for reactive power compensation, reducing voltage sag, swell, and THD. A novel High-gain SEPIC-ZETA converter is designed in this study to enhance the voltage obtained from the PV system with high efficiency. On the other hand, the proposed work aims to improve PQ issues using DSTATCOM with PV-Wind and battery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point (MPPT) controller is adopted for tracking optimal power from the PV and to tune the parameters of this controller adopting Whale optimization algorithm (WOA). Moreover, the PWM rectifier is employed to convert the AC supply from the wind energy system to DC and it is controlled by the Proportional Integral (PI) controller. Hysteresis Current Controller (HCC) is incorporated to generate reference current based on Synchronous Reference Frame (SRF) theory, which is required for the reduction of harmonics. Three phase voltage source inverter is integrated to convert DC-AC supply for distributing the energy supply to load application. Finally, the anticipated work is implemented in MATLAB/Simulink and the comparative analysis is made towards the conventional topologies to authenticate the proficiency of the developed work. As a consequence, the outcomes demonstrate the improved PQ with a lower THD value of 0.72% with high tracking efficiency is accomplished by the proposed technique.