bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Analysis of the Influence of Tunnel Undercrossing Buildings on Pile Foundation
Authors: Yao Huang, Tao Wen Le Yang, Li Zheng,
Published: April 2024

The construction of subways inevitably involves underpass buildings. To ensure the stability of buildings, taking the undercrossing buildings by the Chongqing Rail Transit Line 5 branch as an example. A three-dimensional numerical model of the pile foundation and tunnel was established using FLAC3D. The simulation results show: In the 1-1 profile, the maximum displacement is about 2.28 mm, and the displacement gradually decreases along the top of the tunnel. The maximum displacement in the profile is about 2.28 mm, and the displacement at the bottom of the pile foundation is about 0.3 mm.In the 2-2 profile, the maximum displacement is about 2.27 mm. At the building's pile foundation, the displacement is about 0.2 mm, and at the bottom of the pile foundation, the displacement is about 0.3 mm.In the 3-3 profile, the maximum displacement is 2.26 mm, located at the crown of the tunnel. The displacement at the building's pile foundation is less than 0.2 mm. Based on the analysis of the data from the FLAC3D three-dimensional numerical simulation results, The excavation of the tunnel has a minimal impact on the pile foundations of the aforementioned buildings, and the safety and stability of the buildings are not affected.
Study on Comparative Analysis of Production of Dry Fruit Economic Forest Products Based on Big Data
Authors: Tianke Liu Dong Meng,
Published: April 2024

The analysis of economic forest products production in Shandong Province mainly focuses on economic forest production and output value as well as economic forest plantation area, capital and labour input. By analysing the above, the basic situation of economic forest products production in Shandong Province can be understood on a macro level, laying the foundation for the subsequent study of the relevant data of the farmers, and at the same time providing a reference for the relevant management. There is an obvious gap between the production value of economic forest products in Shandong Province and that of the whole country. By combining modern information technologies such as 3S, Internet of Things, artificial intelligence, big data, cloud computing and other modern information technologies, we have established an integrated modern forest cultivation technology system ranging from the intelligent evaluation of forest terrain conditions, ecological environment simulation and simulation to the precise monitoring and diagnosis of forest growth, intelligent equipment and variable irrigation and fertilisation, etc. It is of great practical significance to expand the scale of production value of economic forest products in Shandong Province and narrow the gap with the national average level by carrying out research on the production and management efficiency of economic forest products in Shandong Province.
Research Of Singing With Note-name Recognition Algorithm Based On End-to-End Model
Authors: Haiyang Ni, Ning Wang, Ming Zhao Bintao Deng, Shimin Wang, Xiaodan Ren,
Published: April 2024

With the rapid development of artificial intelligence, and people are not only seeking material needs, but also pursuing happiness and spiritual aspirations. Especially some singing talent shows appear, but there are many controversial results. In order to solve the problem of singing with note-name evaluation and evaluate the singer's singing level in the professional music education of solfeggio and ear training, the paper uses artificial intelligence algorithms to recognize the note-name. Firstly, preprocess the singing sound data, including pre emphasis, framing, and windowing to reduce the impact of noise, and extract the acoustic features of the singing sound, the acoustic speech features are organized into a form suitable for DCNN input and a DCNN model is designed to obtain the pinyin form of the singing sound, the softmax layer adopts an end-to-end CTC structure, which classifies the input singing speech, finds corresponding phoneme sequences, and obtains the output results. The end-to-end CTC structure is used to classify and optimize the recognition process, and finally the state of the obtained features is output. And then singing pronunciation dictionary generates candidate word sequences based on the mapping relationship between phonemes and notes. Finally, based on the acoustic model score, the candidate note sequence with the highest score is obtained through decoder processing, and obtained the singing with note-name result of the singing sound. In order to further improve recognition performance, the paper introduces a new CTC-DCNN acoustic model. In this model, residual blocks can transfer input features to the output part through shortcuts, allowing the multi-layer convolutional speech features to be preserved as much as possible. At the same time, the deep structure can also better achieve the extraction and analysis of speech features. A new and improved CTC-DCNN acoustic model is obtained by optimizing the maxout function. The algorithm proposed in this paper is fair and equal in obtaining information, and no one participates in the entire process of obtaining scores. It is believed that the scoring results obtained in this way should be more objective.
Self-efficacy of Data Literacy among Primary and Secondary School Teachers in Guangdong Province
Authors: Xun Murong Miaomiao Zeng, Lu Xiang,
Published: April 2024

The application of educational big data provides new basis for teachers to improve teaching and promote student development. However, there are still many challenges in using data to solve complex educational problems. Teacher data literacy has become the key to transforming educational data into effective teaching strategies. The study sought to gain a better understanding of the current state of primary and secondary school Teachers self-efficacy of data literacy and put forward some suggestions for teacher data literacy training. This study used a quantitative methodology. 813 primary and secondary school teachers from Guangdong Province, China, completed a questionnaire on self-efficacy of data literacy. (1) Primary and secondary school teachers reported significantly high levels of self-efficacy in data literacy (mean=3.624). Among the dimensions, the data awareness and ethics dimension had the highest self-efficacy (mean=3.738) and the data research dimension had a lower self-efficacy (mean=3.358). (2)There were significant differences in the primary and secondary school teachers’ self-efficacy of data literacy with different ages, educational backgrounds, and school locations. Primary and secondary school teachers generally recognized the importance of data literacy training. Older teachers who worked in rural schools and had low levels of education have the lowest level of self-efficacy for data literacy. For primary and secondary school teachers' low level of data analysis skills and weak data transformation strategy ability, teacher data literacy training adopts an embedded teamwork approach, integrating multiple resources to build a data team, integrating big data technology throughout the entire teaching process, and helping teachers to use new data and technologies to understand students' needs in a timely manner, to select teaching methods, and to solve real-world problems. It makes data literacy the foundation of teachers' teaching and promotes the development of all students.
Mapping Knowledge Domains of Digital Labor Research: Current Status, Hotspot and Frontier
Authors: He Huang, Weijun Huang Linli Zhu, Xinbin Ge,
Published: April 2024

With the rapid development of digital technology, the mode of production has been transformed into digitalization, virtualization and technologization. The labor has been changed from the real world to the virtual digital world, and digital labor has become an important part of the labor form in modern society. According to bibliometrics and Mapping Knowledge Domains, this study aims to reveal the research status, hot spots and future development trends in the field of digital labor. Based on this, this study takes the relevant core literature from 1990-2023 includes Web of Science Core Collection and Chinese CNKI as the database, and uses CiteSpace visualization software to conduct comparative analysis. The results of this study show that: the number of publications on digital labor in China is lagging behind that of the international community; the research on digital labor in China started late, and the influence of the literature is weaker than that of the international community; the growth rate of relevant literature in Chinese is faster, and the distribution of disciplines is concentrated, while the literature in English is increasing year by year and is relatively stable, and the distribution of disciplines is wide; there is a partial overlap between Chinese and international digital labor research hotspots, with digital labor being studied under the perspectives of computer science, communication studies, education, and so on; Chinese research still focuses on general theoretical discussions, while international research has been widely conducted in the fields of social media, deep learning, digital labor education, artificial intelligence, computer science and information systems. This study not only provides a theoretical perspective for understanding the interaction between digital technologies and labor, but also offers new perspectives for future research and labor education to address the challenges of digital transformation.
A Comprehensive Analysis of Exploring the Efficacy of Machine Learning Algorithms in Text, Image, and Speech Analysis
Authors: T. Prem Jacob J Jinu Sophia,
Published: April 2024

Our study demonstrates a comprehensive investigation of multiple machine learning models based on text, image, and speech analysis. More specifically, concerning text analysis, we studied the application of such models as Recurrent Neural Networks, Gated Recurrent Units, and Bidirectional Encoder Representations from Transformers in classifying documents into pre-defined categories. The outcomes revealed that RNNs observed the highest precision, recall, F1 score, as well as the highest accuracy. Importantly, the models are well-suited to detect sequential dependencies and create semantic representations on the basis of textual data. With regard to image analysis, we found out that Convolutional Neural Networks were the best model. At the same time, VGG16 and GANs also demonstrated rather promising results suggesting that deep learning is paramount to extract significant data features. As far as speech analysis is concerned, we found out that CNNs are exceptional in terms of accuracy to recognize speech patterns in comparison with the other models. Simultaneously, LSTM also observed a high level of accuracy allowing to capture temporal dependencies in audio signals. In conclusion, the findings of our study suggest that it is exigent to identify an appropriate machine learning model depending on the task and the selected dataset. It is also crucial to understand the nature of each of the studied models to assess their applicability for a specific task. Moreover, our study might be valuable for other researchers given that it contributes to the development of the field of deep learning and, thus, promotes the emergence of new applications in different domains.
Analysis of Multi-Intelligent Distributed Japanese Language Block Recognition Based on Knowledge Recognition Corpus
Authors: Huang Jianna
Published: April 2024

The Knowledge Recognition Corpus (KRC) serves as a comprehensive repository designed to capture and organize diverse forms of knowledge for computational analysis and understanding. This corpus encompasses a wide array of data sources, including text documents, images, videos, and structured datasets, covering various domains and topics. The concept of Multi-Intelligent Distributed Knowledge Recognition Corpus (MI-DKRC) represents an innovative approach to harnessing the collective intelligence of distributed systems for knowledge recognition tasks. This paper presents an analysis of Multi-Intelligent Distributed Japanese Language Block Recognition (MI-DJLBR) based on the Knowledge Recognition Corpus (KRC), employing multi-factor bi-gram Sentimental Classification (MFbi-SC). The MI-DJLBR system is designed to recognize and classify Japanese language blocks within a distributed framework, leveraging the collective intelligence of multiple intelligent agents. Through the utilization of the KRC, which encompasses a diverse array of Japanese text samples, images, and multimedia content, MI-DJLBR aims to enhance the efficiency and accuracy of Japanese language block recognition tasks. The incorporation of MFbi-SC further refines this process by considering multiple factors, including syntax, semantics, context, and sentiment, to classify Japanese language blocks with greater precision. Simulation demonstrated that a dataset of 1,000 Japanese language blocks, MI-DJLBR achieves an average recognition accuracy of 94.5%, demonstrating its effectiveness in accurately identifying and classifying Japanese text segments. Furthermore, the incorporation of MFbi-SC enhances the system's classification accuracy by an average of 7.2%, indicating the significance of multi-factor sentiment analysis in refining classification outcomes. In terms of computational efficiency, MI-DJLBR exhibits impressive processing times, with an average recognition speed of 200 blocks per second. This highlights the scalability and responsiveness of the distributed framework, enabling efficient processing of large-scale Japanese language datasets in real-time scenarios.
An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection
Authors: Han Peng
Published: April 2024

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods
Simulation and Optimization of Landscape Planning and Design Based on Virtual Reality Technology
Authors: Qiuyue Shan Xuen Hou,
Published: April 2024

Virtual reality (VR) is transforming the landscape design and planning industry by offering immersive and interactive tools for visualization and decision-making. With VR technology, designers and planners can create realistic 3D environments that allow stakeholders to experience proposed designs in a virtual space before implementation. This enables them to explore different design options, evaluate spatial relationships, and assess the impact of various elements such as vegetation, topography, and infrastructure. VR also facilitates collaboration among project stakeholders by providing a shared platform for communication and feedback. By immersing themselves in virtual landscapes, clients, designers, and stakeholders can better understand design concepts and make informed decisions, leading to more efficient and successful projects. This paper introduces a novel approach to simulating and optimizing landscape planning and design using virtual reality (VR) technology, enhanced by Whale Swarm Optimization Classification (WSOC). The proposed framework aims to leverage the immersive and interactive capabilities of VR to facilitate more effective landscape planning and design processes. Through simulated experiments and empirical validations, the efficacy of the WSOC-enhanced VR-based approach is evaluated. Results demonstrate significant improvements in optimization efficiency and landscape design quality compared to traditional methods. For example, the WSOC model achieved a 40% reduction in design iteration time and a 25% increase in landscape sustainability metrics. Additionally, the framework enabled stakeholders to visualize and interact with proposed designs in real-time, leading to more informed decision-making and consensus-building. These findings highlight the potential of VR technology with WSOC in revolutionizing landscape planning and design, fostering sustainable and aesthetically pleasing outdoor environments.
Design and Implementation of English-Chinese Translation Teaching Platform Based on Deep Learning
Authors: Zhang Ping
Published: April 2024

The Translation Teaching Platform based on Deep Learning represents a groundbreaking advancement in language education, offering students immersive and personalized learning experiences. By leveraging deep learning algorithms, this platform facilitates real-time translation practice, allowing students to engage with authentic language materials and receive immediate feedback on their performance. Through interactive exercises, multimedia resources, and adaptive learning modules, students can enhance their language proficiency in a dynamic and engaging environment. Moreover, the platform's ability to analyze and adapt to individual learning styles enables personalized instruction tailored to each student's needs. With the Translation Teaching Platform based on Deep Learning, language educators can revolutionize their teaching methods and empower students to achieve fluency and proficiency in their target languages. This paper presents the design and implementation of an innovative English-Chinese Translation Teaching Platform based on Deep Learning, augmented by Embedding Feature Probabilistic Segmentation Classification (EF-PSC). The platform leverages advanced deep-learning techniques to provide students with immersive and interactive translation practice opportunities. Through simulated experiments and empirical validations, the efficacy of the platform is evaluated, demonstrating significant improvements in translation accuracy and learning outcomes compared to traditional methods. The EF-PSC model achieved an average accuracy rate of 91% in translating English to Chinese texts, outperforming conventional translation tools by 15%. Additionally, the platform's adaptive learning features enable personalized instruction tailored to each student's proficiency level and learning pace, leading to more effective language acquisition. These findings underscore the potential of deep learning with EF-PSC in revolutionizing translation teaching methods and enhancing language education.
Hybrid Teaching Model of College English Based on Collaborative Filtering Recommendation Algorithm
Authors: Zou Li
Published: April 2024

A recommendation system is a powerful tool that analyzes user preferences and behaviors to provide personalized suggestions or recommendations. By leveraging algorithms and data analysis techniques, recommendation systems help users discover relevant content, products, or services tailored to their individual interests and needs. Whether recommending movies, music, books, or products, these systems enhance user experience by minimizing the effort required to find relevant information or items. Additionally, recommendation systems benefit businesses by increasing customer engagement, satisfaction, and sales through targeted recommendations. The recommendation systems play a crucial role in optimizing decision-making processes and enhancing user satisfaction in various domains. This paper introduces a novel hybrid teaching model for college English, integrating the Collaborative Filtering Recommendation Algorithm with Hybrid Ant Whale Optimization Collaborative Filtering (HAWO-CF). The proposed model aims to personalize English language instruction by leveraging collaborative filtering techniques to recommend tailored learning materials and activities to students. Through simulated experiments and empirical validations, the efficacy of the HAWO-CF-enhanced hybrid teaching model is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional methods. For instance, students using the HAWO-CF model exhibited a 25% increase in vocabulary retention and a 30% improvement in reading comprehension scores. Additionally, the model enabled instructors to optimize teaching strategies based on real-time feedback and performance data, leading to more effective and adaptive instruction. These findings underscore the potential of hybrid teaching models with HAWO-CF in revolutionizing college English education, and fostering personalized and impactful learning experiences.
Visual Design and Evaluation of Public Space Scene Based on Virtual Reality Technology
Authors: Wang Mo
Published: April 2024

Public space scenes are being transformed by virtual reality (VR) technology, offering immersive and interactive experiences that redefine how people engage with their surroundings. Through VR simulations, users can explore and interact with virtual public spaces in ways that were previously impossible. From virtual parks and plazas to digital replicas of iconic landmarks, VR technology allows users to experience the ambiance and atmosphere of public spaces from the comfort of their own homes. Moreover, VR enables designers and planners to experiment with different layouts, amenities, and features, facilitating participatory design processes and community engagement. This paper presents a visual design and evaluation framework for public space scenes using virtual reality (VR) technology, enhanced by Virtual Parallel Edge Hashing Computing (VPE-HC). The framework aims to optimize the design and user experience of public spaces by leveraging VR technology to create immersive and interactive simulations. Through simulated experiments and empirical validations, the effectiveness of the VPE-HC-enhanced VR-based design and evaluation process is evaluated. Results demonstrate significant improvements in user satisfaction, engagement, and usability compared to traditional design methods. For instance, users interacting with public space scenes designed using VPE-HC reported a 40% increase in perceived safety and a 25% improvement in overall satisfaction ratings. Additionally, the framework enabled designers to optimize spatial layouts and amenities based on real-time user feedback and performance data, leading to more effective and user-centric design solutions. These findings underscore the potential of VPE-HC in enhancing the visual design and evaluation of public space scenes based on VR technology, facilitating more inclusive and user-friendly urban environments.
Keyword Analysis of College Counsellor Talking Based on Big Data Technology
Authors: Zi Fei
Published: April 2024

College counselling sessions are being revolutionized by the integration of big data technology, offering students personalized and data-driven guidance throughout their academic journey. By leveraging big data analytics, college counsellors can access comprehensive insights into students' academic performance, interests, and aspirations. This wealth of information enables counsellors to tailor their advice and recommendations to meet the unique needs and goals of each student. Moreover, big data technology allows counsellors to identify trends and patterns in student behavior, facilitating proactive interventions to address challenges and enhance success. This paper presents a novel approach for keyword analysis of college counsellor conversations using big data technology, enhanced by Stop Feature Multi-Dimensional Stacked Deep Learning (SFMD-SDL). The proposed methodology aims to extract valuable insights from counseling sessions by analyzing keywords and patterns in counsellor-student interactions. Through simulated experiments and empirical validations, the effectiveness of the SFMD-SDL-enhanced keyword analysis approach is evaluated. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods. For example, the SFMD-SDL model achieved an average accuracy rate of 90% in identifying key keywords related to student concerns and aspirations. Additionally, the framework enabled counselors to identify emerging trends and patterns in student behavior, facilitating proactive interventions and personalized guidance. These findings underscore the potential of SFMD-SDL in enhancing the effectiveness of college counselor conversations and improving student outcomes.
Designing A Distance Learning System for English Reading by Applying Multimodal Discourse Analysis Theory
Authors: Huang Cheng
Published: April 2024

Multimodal Discourse Analysis (MDA) theory is a comprehensive framework that examines communication beyond just language, considering various modes of expression such as images, sounds, gestures, and typography. This theory recognizes that communication is inherently multimodal, and meaning is conveyed through the interplay of different modes. MDA analyzes how these modes interact within texts and contexts to create meaning, emphasizing the importance of visual, auditory, and spatial elements alongside linguistic features. By examining multimodal texts, MDA provides insights into how meaning is constructed, negotiated, and understood in diverse communicative contexts, from advertising campaigns to political speeches. This paper proposes the design of a distance learning system for English reading by applying Multimodal Discourse Analysis (MDA) theory, augmented with Distance Vector Space Coordinate Classification (DVS-CC). The system aims to provide an immersive and interactive learning experience by integrating various modes of communication, including text, images, audio, and video. Through simulated experiments and empirical validations, the effectiveness of the DVS-CC-enhanced distance learning system is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional distance learning approaches. For example, students using the DVS-CC-enhanced system achieved an average score increase of 25% in reading proficiency assessments. Additionally, the system's adaptive learning features enabled personalized instruction tailored to each student's learning style and pace. These findings underscore the potential of applying MDA theory with DVS-CC in designing effective distance learning systems for English reading, fostering enhanced learning experiences in remote educational settings.
The Countervailing Effect Of "Digital Humanistic View" On Translation Teaching Under Computer-Assisted Technology
Authors: Hui Li Zhixiang Gao,
Published: April 2024

"Digital Humanistic View" on Translation Teaching under Computer-Assisted Technology emphasizes the integration of humanistic values with digital tools in translation education. This approach recognizes the importance of technology in enhancing translation teaching and learning experiences while also emphasizing the significance of human creativity, critical thinking, and cultural understanding. By leveraging computer-assisted technology, translation instructors can provide students with access to diverse resources, interactive exercises, and automated translation tools to facilitate language acquisition and proficiency. However, the "Digital Humanistic View" emphasizes that technology should complement rather than replace human expertise, encouraging students to engage deeply with language, culture, and context in their translation practice. This paper explores the countervailing effect of the "Digital Humanistic View" on translation teaching under computer-assisted technology, enhanced by Hidden Markov Translation Teaching (HMTT). The study investigates how the integration of humanistic values with digital tools in translation education influences teaching methods and student learning outcomes. Through simulated experiments and empirical validations, the impact of HMTT on translation teaching is evaluated, with a focus on student engagement, proficiency, and cultural understanding. Results demonstrate that while computer-assisted technology offers valuable resources and efficiency in translation teaching, the "Digital Humanistic View" emphasizes the importance of human creativity, critical thinking, and cultural sensitivity. Analysis stated that students exposed to HMTT with the "Digital Humanistic View" reported a 30% increase in cultural understanding and a 20% improvement in translation accuracy. Additionally, the framework enabled instructors to balance the benefits of technology with the humanistic aspects of translation, fostering a holistic approach to language education. These findings highlight the potential of HMTT with the "Digital Humanistic View" in shaping effective and ethical translation teaching practices in the digital age.
Application of Deep Learning Technology in English Practical Teaching
Authors: Yang Yang Zhiyan Shi,
Published: April 2024

Practical teaching emphasizes hands-on learning experiences that engage students in real-world applications of theoretical knowledge. Through practical teaching methods, students gain valuable skills, problem-solving abilities, and critical thinking capacities essential for their future careers. Practical teaching often involves interactive activities, experiments, projects, and simulations tailored to the subject matter, allowing students to explore concepts in depth and develop a deeper understanding of the material. This paper explores the application of deep learning technology in English practical teaching, leveraging Stacked Logistic Deep Learning (SLDL). The study aims to enhance the effectiveness of English language instruction by integrating deep learning techniques into practical teaching methodologies. Through simulated experiments and empirical validations, the efficacy of SLDL-enhanced practical teaching approaches is evaluated. Results demonstrate significant improvements in student proficiency and engagement compared to traditional methods. Simulation analysis expressed that students exposed to SLDL-enhanced practical teaching methods achieved an average score increase of 20% in English language assessments. Additionally, the SLDL model facilitated personalized learning experiences tailored to individual student needs, leading to more effective language acquisition and retention. These findings underscore the potential of deep learning technology, particularly SLDL, in revolutionizing English practical teaching and fostering enhanced learning outcomes.
Biometric-Driven Digital Village Governance: A SWOC-Enabled Model with Fraud Prevention
Authors: Li Haili
Published: April 2024

In the digital age, the integration of advanced technologies and biometrics plays a pivotal role in transforming governance models. This research introduces an innovative approach to digital village governance, leveraging the potential of biometric data in biomedical applications while incorporating robust fraud detection mechanisms. The proposed model harnesses the power of Swarm Fish Optimization Classification (SWOC) to enhance the accuracy and efficiency of biometric-based authentication systems, ensuring the security of village governance processes. The study outlines the multifaceted components of the Intelligent Digital Village Governance Model, emphasizing its adaptability to the unique needs of rural communities. Central to this model is the utilization of biometric data, such as fingerprints, for user identification, access control, and the delivery of essential services. SWOC, with an optimization and classification algorithm inspired by swarm behavior, is integrated to refine the accuracy of biometric identification and detect fraudulent activities. Experimental results demonstrate the efficacy of SWOC in enhancing the accuracy of biometric-based authentication, thereby strengthening the security of digital village governance. The model's adaptability, scalability, and compliance with ethical standards are also discussed, ensuring responsible deployment in rural settings.
A Quasi-Cyclic LDPC Based Low Complexity and Area-Efficient Communication System for IEEE 802.11n
Authors: Yashika Gaidhani, Tejaswini Panse Monica Kalbande,
Published: April 2024

Low-density parity-check (LDPC) code, which has an excellent error-correcting performance that is close to the Shannon limit, is the most often used error correction code (ECC) for reliable and effective communication. Despite higher performance and lower decoding complexity, the main disadvantage of LDPC codes is their high encoding complexity. A significant problem is the VLSI implementation of the LDPC encoder and decoder. In this paper, structured LDPC codes—also known as quasi-cyclic low-density parity check codes—have been used since it is good bit error ratio (BER) performance and adaptable hardware execution characteristics. The Complete (Encoder-Channel-Decoder) communication system with a 1/2 code rate, 648 bits codeward length, and sub-block size of z = 27 has been constructed for the IEEE 802.11n wireless standard.
Investigating the Impact of Technology Scaling on Stability Performance of Sub-threshold Static Random Access Memory Bitcell Topologies
Authors: Vijay N. Patil, Yuvraj V. Parkale Ajjay S. Gaadhe,
Published: April 2024

Static random-access memory (SRAM) is increasingly being used in VLSI circuits as a result of the development of portable devices. As the demand for low-power devices increases, sub-threshold operation of SRAMs has become a popular method to reduce power consumption but at the cost of reduced stability. The work aims to propose design parameters for SRAM bit cell topologies- 6T and 10T with proper device sizing and to investigate the impact of technology scaling on its stability performance in the sub threshold region. Moreover, analysis demonstrates the impact of variations of device sizing in terms of Cell ratio and Pull up ratio on stability margins of designed SRAM topologies. Simulations are carried out in the HSPICE environment using 16 nm and 22 nm CMOS process nodes. We find that the stability margins of 10T SRAM cell outperform the 6T SRAM cell; however stability margin decreases with decreasing technology nodes. The Read Static Noise Margin (RSNM) and Write Static Noise Margin (WSNM) of 10T SRAM cell is improved by 48.38% and 308.64% respectively in 22nm and 53.04% and 243.93% respectively in 16nm process technology as compare to 6T SRAM cell.
A Novel IoT-Enabled Device for Early Prediction and Detection of Chronic Disease
Authors: Shubhangi Rathkanthiwar, Prachi Gawande Yogesh Kale,
Published: April 2024

Chronic diseases is increasing day-by-day according to world health organization (WHO), and now its health challenge worldwide that requires early prediction and detection of accurate diagnosis of a chronic diseases. In this paper is to design a device to get the reading from medical sensor calculate the early warning score (EWS) by using machine learning algorithms. The main target of this paper is to offer actual readings of medical sensor which gives blood pressure (BP) and pulse rate (PR) readings as per the prescribe time interval suggest by the medical professionals. EWS calculated by using machine learning (ML) algorithms, this paper includes K-NN, Naïve Bayes, support vector machine, and random forest for early prediction of chronic diseases. In this Naïve Bayes, gives 99.44% accuracy result which is a crucial aspect of in case of ICU admission due to chronic diseases. The paper target to decrease healthcare expenses and identify wrongdoings in the chronic diseases.
A Novel Data Analysis Architectural Framework for Data Reduction on Edges for Smart City Healthcare
Authors: Bhramaramba Ravi Swapnaja Rajesh Hiray,
Published: April 2024

The most difficult task is managing and digesting the massive amount of heterogeneous data created by IoT. It questioned the efficiency and computational storage capacity of today's infrastructure resources. Furthermore, because there is so much data, it becomes difficult to give the user enough information to make a decision. A technology known as Complex Event Processing (CEP) has been created to glean important insights from massive real-time data streams. The goal of this research is to create a system that can extract complicated events from large amounts of sensor data and only transmit those events to a cloud server. Global healthcare services have been expanding at an exponential rate because of the vast amount of data that clinical and medical organizations generate daily. Healthcare systems can use e-health services to meet the medical and assistive requirements of people. Edge technology is proven in this direction. Mixing the old classic concepts with the innovative we have proposed the Novel data analysis architecture for smart heart disease prediction. We have created the fuzzy-based inference system at the edges for the data analysis and used the resulting Complex Events at the end predictions.
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.
Enhancing MANET Performance: A Novel Approach Through Nature-Inspired Scheduling Algorithms
Authors: Syed Wajahat Abbas Rizvi,  Soumen Kanrar Abhishek Shukla,
Published: April 2024

In the rapidly evolving landscape of Mobile Ad Hoc Networks (MANETs), the quest for optimal performance through efficient scheduling remains paramount. This paper, titled "Enhancing MANET Performance: A Novel Approach Through Nature-Inspired Scheduling Algorithms," introduces an innovative methodology that leverages the intricate mechanisms of nature to address the complex challenges inherent in MANET scheduling. Drawing inspiration from the adaptive behaviors observed in natural systems, we have developed a suite of nature-inspired algorithms—encompassing the genetic algorithm, particle swarm optimization, and ant colony optimization—tailored specifically to enhance the scheduling efficiency within the dynamic and decentralized context of MANETs.

Our research undertakes a comprehensive evaluation of these algorithms against traditional scheduling methods, showcasing a notable improvement in key performance metrics such as network throughput, latency, and energy consumption. The novel contribution of this study lies in the adaptation and optimization of these bio-inspired algorithms for the unique demands of MANET scheduling, culminating in the development of a proprietary analytical model that significantly outperforms existing strategies. Through a rigorous comparative analysis, our results illuminate the superiority of our approach, demonstrating not only enhanced network efficiency but also improved scalability and reliability under varying conditions.

The implications of this research extend beyond the immediate enhancements to MANET performance, proposing a paradigm shift in how scheduling challenges are approached within ad hoc networks. By bridging the gap between biological principles and technological applications, this study paves the way for future innovations in network management, offering a blueprint for the development of more adaptive, robust, and efficient networking solutions.
Credit Risk Assessment and Fraud Detection in Financial Transactions Using Machine Learning
Authors: Rakesh Pandit, Sheetal Bawane, Jayesh Surana Pankaj Malik, Ankita Chourasia,
Published: April 2024

Credit risk assessment and fraud detection are crucial tasks in the financial industry, vital to preserving financial organizations' legitimacy and sustainability. Traditional methods often fall short in accurately assessing risk and detecting fraudulent activities in a timely manner. In recent years, machine learning has emerged as a powerful tool for enhancing these processes, leveraging great dimensions of transactional statistics and superior algos for making more informed decisions. This research paper explores the usage of ML techniques in credit risk assessment and fraud detection within financial transactions.

The paper begins with an overview of the importance of accurate risk assessment and fraud detection in financial transactions and introduces the role of machine learning in addressing these challenges. A comprehensive literature review is conducted to analyze existing methodologies, algorithms, and research trends in the field. Data acquisition and preprocessing techniques are discussed, emphasizing the importance of clean and relevant data for model training. Feature engineering strategies are explored to extract meaningful information from financial transaction data and enhance the predictive capabilities of machine learning models.

Various machine learning algorithms suitable for credit risk assessment and fraud detection are examined, including LR, SVMs, RF, DTs and DNNs. The efficacy of these techniques is evaluated by discussing model metrics for assessment and ensemble approaches for boosting efficiency, with a focus on metrics such as accuracy, precision, recall, and ROC-AUC.

The paper presents case studies and experimental results illustrating the application of machine learning models in real-world scenarios, highlighting their effectiveness in improving risk assessment and fraud detection processes. Additionally, difficulties such as imbalanced datasets, comprehensibility of the model and adherence to regulations are discussed, along with potential research directions and future trends in the field.

In conclusion, this research emphasizes the transformative potential of machine learning in credit risk assessment and fraud detection within financial transactions. By leveraging advanced algorithms and data-driven approaches, financial institutions can enhance their decision-making processes, mitigate risks, and safeguard against fraudulent activities, ultimately contributing to a more secure and resilient financial ecosystem.
Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm
Authors: Agees Kumar C. Selvin Prem Kumar S.,
Published: April 2024

Brain tumour segmentation is one of the most challenging problems in medical image analysis. To detect brain cancers, radiologists must use a computer-based tumour classification model. In medical imaging research, several computer-aided diagnostic (CAD) models are available to help radiologists with their patients. The reason for brain growth division is to deliver exact outline of brain tumour regions. This study suggests a Walrus Optimisation Algorithm approach for detecting brain tumours using MRI (Magnetic Resonance Imaging). Separating the characteristics into four categories has been utilized: no growth, gliomas, meningiomas, and pituitary cancers. The deep learning based model inception AlexNet and ResNet-18 is trained on an augmented training dataset. The CNN classifier is used for characteristics map improvement, while the LSTM (Long momentary memory) classifier is utilized for order. Besides, the boundary remembered for the classifiers is chosen aimlessly utilizing the Walrus Improvement Calculation to build the presentation of the CNN-LSTM classifier. The performance of the tumour diagnosis is assessed using the metrics: overall classification accuracy of 98.8%, precision of 96.23%, recall of 97.01%, specificity of 98%.
An Advanced CNN based System for Rice Plant Diseases Detection and classification in Precision Agriculture
Authors: Poonam Gupta, Surendra Waghmare Priya Ujawe,
Published: April 2024

For the vast majority of people on the planet, agriculture is one of the most crucial industries. Rice is a vital food source, but it also faces serious risks from illnesses that can affect yield quality and quantity, potentially resulting in 20% to 40% crop losses. Reducing production losses requires early diagnosis of these disorders. For farmers, however, it is not possible to manually monitor such large areas of field. The proposed model based on a Convolutional Neural Network of Resnet50 and transfer learning model, specifically a modified TLResnet152V2. Transfer learning-based weights are used for precise identification and categorization and detection for rice leaf diseases. The proposed system demonstrates high accuracy in identifying four classes of rice diseases viz: Brown spot, Healthy, LeafBlast, NeckBlast. It achieves a remarkable accuracy of Resnet50 & TLResnet152V2 is 93.20% and 95.03 % for normalized augmented dataset respectively. Comparative analysis reveals that modified approach outperforms similar methodologies applied to the Bangladesh rice dataset or datasets of comparable size given in the existing research work. Overall findings underscore the efficacy of proposed approach in advancing the precision of rice plant disease detection, offering a promising solution to enhance agricultural practices and mitigate production losses.