bluuebunny/crossref_metadata_2025 huggingface dataset:
О понятии критики и о критическом методе у Канта
Authors: Krouglov Alexei
Published: September 2023

После абриса предыстории кантовского понятия критики и выявления наиболее распространенных значений критики на примере сочинений Ж. Леклерка, И.Г. Вальха, И.Кр. Готшеда и Ж.-Ф. Мармонтеля дана краткая характеристика наиболее самобытных черт кантовского понятия критики, максимально приближенная к кантовским оригинальным произведениям, опубликованным после 1781 года. Своеобразие кантовского зрелого понятия критики состоит в ее направленности на саму познавательную способность, в сравнении ее действия с судебным процессом, в акцентировании отсутствия на ее пути каких-либо преград, включая сферу религии, права и морали, в связи с требованием свободы мысли и свободы слова и в описании собственной эпохи как века критики. Наряду с общей характеристикой кантовской критики в целом показан также специфический метафизический смысл критики, согласно которому она представляет собой срединный путь между догматизмом и скептицизмом. Отталкиваясь от метафизического смысла критики, автор определяет кантовское понятие критического метода, направленного на преодоление догматизма и скептицизма путем обнаружения общих свойственных им обоим и объединяющих их друг с другом заблуждений. Непонимание и забвение кантовских новаций в развитии критики демонстрируется на примере популярного ныне так называемого «критического мышления», нашедшего определенное применение в российском образовательном процессе. Указанное «критическое мышление» ведет свою родословную от Дж. Дьюи и почти полностью игнорирует кантовские достижения.
Макс Шелер в поисках идеи человека
Authors: Loginov Aleksandr
Published: September 2023

Рецензия на: Шелер М. К идее человека / пер. с нем. А.Н. Малинкина. — М., СПБ. : Центр гуманитарных инициатив, 2022.
Review on pharmacological therapies for management of gestational diabetes
Authors: Shabnam Akram , Safia Bibi
Published: September 2023

The prevalence of Gestational Diabetes Mellitus (GDM) is increasing and is closely linked to numerous negative pregnancy outcomes. To mitigate these risks, early identification and management of GDM is critical. Treatment for GDM aims to maintain normal blood sugar levels and typically includes glucose monitoring, adjustments to diet, lifestyle changes, moderate physical activity, and medication as necessary. Insulin administration is generally the preferred choice for pharmacotherapy, but oral drugs such as metformin or glyburide may also be appropriate. Compared to glyburide, which has been linked to higher birth weight, neonatal hypoglycemia, raising the risk of shoulder dystocia, and necessitating a caesarean delivery, metformin is seen to be a safe first-line therapy. It should be emphasized that some expecting mothers choose complementary and alternative therapies, such as traditional herbal supplements and treatments. This review article will address present pharmacological options and considerations associated with treating GDM.
Promoting data element circulation for urban digital economy development
Authors: Qiyi Han , Ying Qin
Published: September 2023

Data element circulation, also known as data circulation, refers to the entire process of continuously collecting, processing, storing, transmitting, and using data in economic and social activities. The rapid development of information technology has made the data elements circulation an important driving force for promoting economic growth, improving production efficiency, and fostering social innovation. However, the data elements circulation also presents a series of challenges, including data value, data security, data quality, data openness, and sharing. To fully unleash the value of data resources, improve the new foundational system, stimulate market vitality, and create a data element market with significant influence, this paper conducts a thematic study on promoting data element circulation. The significance of accelerating data element circulation, the global efforts in data element circulation, and the basic requirements for data element circulation are discussed. The paper also provides recommendations for promoting data element circulation, including policy and regulatory development, infrastructure enhancement, and continuous technological innovation. Furthermore, the paper emphasizes the importance of strengthening application scenarios, such as smart city initiatives and data center services and building a robust support system for data element circulation. The proposed strategies aim to foster high-quality development of the urban digital economy and facilitate the circulation of data elements.
Ecclesiastic Audio Description: The Church from a Semiotic and Translation Perspective
Authors: Lax-López María
Published: September 2023

Audio description (AD) is a type of accessible translation consisting of a process of intersemiotic translations from images into words. Its objective is, mainly, allowing people with visual disabilities to create a mental image of the things they cannot perceive visually. In this study, we will address a type of AD that has not been explored in academic and professional contexts: church AD, where the source text is the church, understood to be the architectonic structure used for Christian worship. Our aim is to provide a basis for the study and practice of church AD. To this end, we propose three approaches: functional and contextual, semiotic and normative. The first approach describes the different ways in which churches are used today and the context in which the audio described text is produced. The second helps to identify the visual keys that form the church's meaning and to guide the audio describer in selecting translatable information. The last one provides AD guidelines or strategies that can be applied to the submodality of AD analyzed.
Developing Cosmopolitanism Through Intercultural Mediation Activities: An After-School Digital Storytelling Project in Catalonia
Authors: Zhang Miaomiao
Published: September 2023

This article analyzes interaction taking place in an after-school digital storytelling project, involving a group of teenagers in Catalonia, Spain, with different lingua-cultural backgrounds. It focuses on intercultural mediation activities carried out in one of the early project sessions in which a young girl of Ghanaian origin mobilizes her previous life experience to mediate, for her local peers in Catalonia, understanding of a video produced by Ugandan youth. The data is transcribed and analyzed using a multimodal conversation analytic perspective. Drawing on the theoretical concepts of intercultural mediation, cosmopolitanism, and funds of knowledge, this article investigates the following: (a) how the girl mobilizes her funds of knowledge to mediate the content of the video and the other audience members and, (b) how cosmopolitanism is developed in intercultural mediation. The article also touches on how intercultural mediation is collaboratively constructed across modes, languages, and material objects. The findings indicate that the young participants’ cosmopolitan stances are enacted and enabled in intercultural mediation, as the youngsters can make sense of cultural concepts that they can not tackle as well on their own. The findings further help to reconceptualize the competences, knowledge, and resources of youth in the superdiverse and interconnected world.
Intelligent Early Diagnosis System against Strep Throat Infection Using Deep Neural Networks
Authors: Kumar K. Antony , R Aruna
Published: September 2023

The most frequent bacterial pathogen causing acute pharyngitis is Group-A hemolytic Streptococcus (GAS), and sore throat is the second most frequent acute infection. The immunological reaction to group A Streptococcus-induced pharyngitis results in Acute Rheumatic Fever (ARF). A genetically vulnerable host for ARF is a streptococcal infection. ARF, which can affect various organs and cause irreparable valve damage and heart failure, is the antecedent to Rheumatic Heart Disease (RHD). RHD, in many countries is Cardiovascular Disease (CVD) refers to a range of conditions that affect the heart and blood vessels, including coronary artery disease, heart attack, heart failure, and stroke. It is important to note that while this approach has demonstrated promising results, further studies and validation are necessary to establish its clinical feasibility and reliability. Further research can also be done to evaluate the generalization of the model to larger and diverse patient populations. The results showed that using Image Synthesis-based augmentation improved the ROC-AUC scores compared to basic data augmentation. The proposed method could be a valuable tool for healthcare professionals to quickly and accurately diagnose strep throat, leading to timely treatment and improved patient outcomes. The experimental findings indicate that the suggested detection approach for strep throat has a high level of accuracy and effectiveness. The approach has an average sensitivity of 93.1%, average specificity of 96.7%, and an overall accuracy of 96.3%. The ROC-AUC of 0.989 suggests that the approach is effective at distinguishing between positive and negative cases of strep throat. These results indicate that the suggested detection approach is a promising tool for accurately identifying cases of strep throat.
Remote Health Monitoring IoT Framework using Machine Learning Prediction and Advanced Artificial Intelligence (AI) Model
Authors: Kallam Suresh , Chakrabarti Prasun , Hung Bui Thanh , S Siva Shankar
Published: September 2023

Real intervention and treatment standards drew attention to remote health monitoring frameworks. Remote monitoring frameworks for disease detection at an early stage are opposed by most conventional works. Even so, it ran into issues like increased operational complexity, higher resource costs, inaccurate predictions, longer data collection times, and a lower convergence rate. A remote health monitoring framework that uses artificial intelligence (AI) to predict heart disease and diabetes from medical datasets is the goal of this project. Patients' health data is collected via smart devices, and the resulting data is then combined using a variety of nodes, including a detection node, a visualisation node, and a prognostic node. People with long-term illnesses (such as the elderly and disabled) are in such greater demand than ever before that a new approach to healthcare delivery is essential. In the evolved paradigm, conventional physical medical services foundations like clinics, nursing homes, and long haul care offices will be old. Due to recent advancements in modern technology, such as artificial intelligence (AI) and machine learning (ML), the smart healthcare system has become increasingly necessary (ML). This paper will discuss wearable and smartphone technologies, AI for medical diagnostics, and assistive structures, including social robots, that have been created for the surrounding upheld living climate. The review presents programming reconciliation structures that are urgent for consolidating information examination and other man-made consciousness instruments to develop brilliant medical care frameworks (AI).
Prevention in Healthcare: An Explainable AI Approach
Authors: Makubhai Shahin , Pathak Ganesh R , Chandre Pankaj R
Published: September 2023

Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems.
Distributed Infrastructure for an Academic Cloud
Authors: Nishant Neerav , Singh Vaishali
Published: September 2023

The various community infrastructure literature reveals the challenges in educational institutions to embrace cloud computing trends. Setting up an own data center in effect means a private cloud. If research on the open cloud services is available within the institution, then the rollout of such research products becomes an in-house implementation. Thus, even reducing the dependence on cloud vendors. Distribution of resources opens the channel for better communication within academic institutions. It also attracts opportunities to procure individual hardware with a bigger gain. Enormous spending and unaccounted credits fall into central budgets if not controlled in a structured manner. Also, increasing the overall data management cost as an institution needs a different perspective for its’ long-term benefits. The expenses allow branching the cloud management tasks either in a vendor’s private cloud or own Cloud if feasible. Bigdata does touch the academics to so much extent that such disparate de-central data management creates several pitfalls. The solution then suggested to have a controlled environment claimed as distributed computing. Infrastructure spending shoots up with a pay as you go model. We claim that a distributed infrastructure as an excellent opportunity in the computing when performed at the cost of trust of a private cloud. The open-source movements experiment the distributed clouds by promoting OpenStack swift.
An Evaluation of Machine Learning and Big Data Analytics Performance in Cloud Computing and Computer Vision
Authors: Abed Hather Ibraheem , Sultan Nawar A. , Mohammed Osama Yassin
Published: September 2023

Although cloud computing is receiving a lot of attention, security remains a significant barrier to its general adoption. Cloud service users frequently worry about data loss, security risks, and availability issues. Because of the accessibility and openness of the huge volume of data amassed by sensors and the web throughout recent years, computer applications have seen a remarkable change from straightforward data processing to machine learning. Two widely used technologies, Big Data and Cloud computing, are the focus of worry in the IT industry. Enormous data sets are put away, handled, and broke down under the possibility of "Big Data." Then again, cloud computing centres around giving the framework to make such systems conceivable in a period and cash saving way. The objective of the review is to survey the Big Data Analytics and Machine learning ideal models for use in cloud computing and computer vision. The programmed data examination of enormous data sets and the production of models for the wide connections between data are the centre highlights of machine learning (ML). The usefulness of machine learning-based strategies for identifying threats in a cloud computing environment is surveyed and compared in this research.
Role of Blockchain Technology Integration for Green Bonds Issuance with Sustainability Aspect
Authors: Misra Neeti , Gupta Sumeet , Sidhu Kawerinder Singh , Kumar Anil , Anandaram Harishchander , Rao T.Joji , Singh Rajesh , Gehlot Anita , Akram Shaik Vaseem , Joshi Kapil
Published: September 2023

Green bonds have gained significant attention in supporting sustainable development goals for achieving sustainability. During the issuance of green bonds, there are a few concerns such as standardization, greenwashing, and lack of benefits that can be gained with green bonds. However, blockchain technology is a promising solution for green bond issuance because it has already shown its impact on different finance activities. This study aims to address and analyze the role and significance of green bond issuance for meeting sustainability with blockchain technology and also suggested recommendations for future research. Decentralized application based on the Algorand blockchain and high-level architecture proposed for the issuance of green bonds is at the primary level. There is no discussion regarding standardizing the environmental data, and the number of benefits gained by the green bond is not addressed in the previously published literature. From the analysis, it has been identified that a similar framework of blockchain cannot be implemented as the geographical and environmental parameters are quite different for every nation. So, every nation needs to customize the framework according to the nation's requirements. This study is the first attempt to combine information from previously published research about green bond issuance and integration of blockchain for green bond issuance, enlightening the disruption caused in the issuance of green.
Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey
Authors: Adagale -Vairagar Shubhangi , Gupta Praveen
Published: September 2023

Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc.
Progressive Faster Residual Convolutional Neural Network for Improving Osteoarthritis of the Temporomandibular Joint Detection
Authors: Krishnamoorthy Vijaya Kumar , Baskaran Santhi
Published: September 2023

Osteoarthritis of the Temporomandibular Joint (TMJ-OA) is a chronic condition that affects the TMJ and is characterized by the progressive degeneration of the internal surfaces of the joint. Several deep learning models were adopted for identifying the TMJ-OA from the panoramic dental X-ray scans. Amongst, an Optimized Generative Adversarial Network (OGAN) with Faster Residual Convolutional Neural Network (FRCNN) produces more synthetic images to train the FRCNN for recognizing TMJ-OA cases. But, its accuracy was comparatively low while recognizing Region-of-Interest (RoI) from the panoramic scans that have analogous objects. Hence in this paper, an OGAN with a Progressive FRCNN (OGAN-PFRCNN) model is proposed, which enhances the FRCNN by integrating the Feature Pyramid Network (FPN) and RoI-grid attention strategy for TMJ-OA identification. First, the training images are fed to the ResNet101 for feature mining, which provides Multi-Scale Feature Map (MSFM) from the dental panoramic scans. Those features are then passed to the FPN with the RoI-grid attention strategy, which encodes richer characteristics by considering standard attention and graph-based point functions into a combined formulation. Then, those characteristics are fused at various levels to get a useful MSFM, which increases the network efficiency significantly. Moreover, such a Feature Map (FMap) is used to train the PFRCNN model, which is later applied to recognize the test scans into either healthy or TMJ-OA. At last, the testing outcomes show that the OGAN-PFRCNN attains 96.2% accuracy on the panoramic dental X-ray database compared to the FRCNN model.
Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model
Authors: Al-Abadi Abdullah Ali Jawad , Mohamed Mbarka Belhaj , Fakhfakh Ahmed
Published: September 2023

Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as  low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity.
A Generalized Renyi Joint Entropy Method for the Detection of DDoS Attacks in IoT
Authors: Mathew Jeethu , Priyadarsini R Jemima
Published: September 2023

Internet of things connects all the smart devices with internet and gain more information in comparison with other systems. Since different types of objects are connected, privacy and security of the users must be ensured. Because of the decentralised nature, IoT is prone to different types of attacks which are either active or passive. Since internet is the main part of IoT, the security issues present in Internet will be available in the Internet of Things too. Distributed denial of service is a major threat of this type and a critical threat. It reduces the performance of the complete network even it breaks entire communication. For this reason many researches have been made in this area to detect Distributed Denial of Service attack. Entropy-based approaches to identify DDoS attacks in the internet of things are discussed in this research. This new approach is based on the GRJE method, which stands for generalised Renyi joint entropy. Renyi joint entropy is used in the suggested approach to analyse network traffic flow. The suggested method is put into practise and evaluated against other methods based on a few factors.  Results from an analysis of the suggested system's effectiveness in NS2 are reported in this study.
Design and Implementation of Auto Switch Lighting Monitoring and Control System
Authors: Maitanmi Stephen O. , Sunday Idowu A. , Yaw Mensah A. , Samuel Okafor , Victor Illuyomade , Simon Sharon
Published: September 2023

The havoc that the electricity can cause may be unimaginable compared with the benefits if not proper controlled, maintained, managed and used. Developed countries has setup good system of management and maintenance of electricity supply. Studies have shown that electricity supply should be properly distributed and managed with respect to the use, however, only a few of these studies were implemented in developing countries including Nigeria. Therefore, this research was aimed at the design and development of auto switch lighting monitoring and control system. The research adopted multiple concepts by making use of the development of software, and hardware configurations. The configurations combined a micro-controller (Arduino), sensor GSM module for wireless communication, battery, Real Time Clock (RTC) and LEDs (Light Emitting Diodes) for display purpose. By considering the time set by Real Time Clock (RTC) and from the status of (Light Dependent Resistor) LDR  sensor, the controller turns the light ON/OFF. The results showed that the photoelectric (presence) sensor turns on the light only when the presence is detected during night time and Current Sensor will sense the current value from which power consumed is calculated. It also provides the information about the faulty lamps which ease the fault detection. The research concluded by ensuring that the objectives were met. The research employed the synchronization of database created and linked with a mobile software that was further linked with a digital board. The designed software was implementation and it was able to control the auto switching, monitoring and control of the lighting system in Babcock University. It was recommended that this software should be replicated for the country.
Automated Optimization Deep Learning Model for Assessment and Guidance System Through Natural Language Processing with Reduction of Anxiety Among Students
Authors: Peng Jing , Macwes Marilyn W.
Published: September 2023

The Assisted Assessment and Guidance System serves as a valuable tool in supporting individuals' learning, growth, and development. The Assisted Assessment and Guidance System with Natural Language Processing (NLP) is an innovative software application designed to provide personalized and intelligent support for assessment and guidance processes in various domains. NLP techniques are employed to analyze and understand human language, allowing the system to extract valuable insights from text-based data and provide tailored feedback and guidance. This paper proposed an Integrated Optimization Directional Clustering Classification (IODCc) for assessment of the foreign language anxiety. Additionally, the paper introduces an Integrated Optimization Directional Clustering Classification (IODCc) approach for assessing foreign language anxiety. This approach incorporates two optimization models, namely Black Widow Optimization (BWO) and Seahorse Optimization (SHO). BWO and SHO are metaheuristic optimization algorithms that simulate the behaviors of black widow spiders and seahorses, respectively, to improve the accuracy of the assessment process. The integration of these optimization models within the IODCc approach aims to enhance the accuracy and effectiveness of the foreign language anxiety assessment. Simulation analysis is performed for the data collected from the 1000 foreign language students. The experimental analysis expressed that the proposed IODCc model achieves an accuracy of 99% for the classification. The findings suggested that through pre-training of languages, the anxiety of the students will be reduced.
Analyzing the Application of Minimalism in Product Appearance Design using Associative Data Mining Optimized Feature Selection and Deep Learning of Bang&Olufsen Products
Authors: Wang Yi , Abidin Ahmad Hisham Bin Zainal
Published: September 2023

The application of minimalism in product appearance design has gained significant attention in recent years due to its focus on simplicity, functionality, and aesthetic appeal. This paper explores the use of Associative Data Mining Optimized Feature Selection (ADM-OFS) classifier with deep learning techniques to analyze the application of minimalism in product appearance design, using Bang&Olufsen products as a case study. The proposed ADM-OFS perform feature selection is performed using an associative data mining approach, which estimates the most relevant and influential features that contribute to minimalistic design. The optimized feature selection process enhances the accuracy and efficiency of the analysis by reducing the dimensionality of the dataset while retaining its essential characteristics. The ADM-OFS model comprises the deep learning techniques employed to capture intricate patterns and relationships between minimalism and product appearance design. The deep learning model is trained on the dataset, enabling it to recognize complex visual features and make predictions about the minimalistic qualities of new product designs. The findings of ADM-OFS provide valuable insights into the application of minimalism in product appearance design, specifically in the context of Bang&Olufsen products. The analysis demonstrated the ADM-OFS classifier with deep learning, in analyzing and interpreting the application of minimalism in product appearance design. The findings of ADM-OFS stated that the designers, manufacturers, and researchers in their pursuit of creating visually appealing and functionally efficient products that embody the principles of minimalism.
Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry
Authors: Jin Xi , Kim Hyuntai
Published: September 2023

Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an  Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components of the Box-Jenkins approach. Autocorrelation and partial autocorrelation plots are analyzed in the identification phase to help choose the best BJ-ARIMA model. The estimation phase involves fitting the selected BJ-ARIMA model to the historical data, using techniques such as maximum likelihood estimation. Finally, BJ-ARIMA diagnostic checking is performed to ensure the model's adequacy and reliability. The findings of BJ-ARIMA analysis will provide a solid foundation for forecasting trends and making informed decisions in the dynamic and evolving world of the popular music industry.
Enhancing Audio Signal Quality and Learning Experience with Integrated Covariance Weiner Filtering in College Music Education
Authors: Yang Mengna
Published: September 2023

In recent years, computer music technology has become increasingly prevalent in college music education, offering new possibilities for creative expression and pedagogical approaches. This paper concentrated on the music education in the colleges with the application of integrated time and frequency filtering (ITFF) with Kalman integrated covariance Weiner filtering in college music education. The ITFF technique combines time and frequency domain analysis to enhance the quality and clarity of audio signals. By integrating the Kalman integrated covariance Weiner filtering, the ITFF method provides robust noise reduction and improved signal representation. This integrated approach enables music educators to effectively analyze and manipulate audio signals in real-time, fostering a more immersive and engaging learning environment for students. The findings of this study highlight the benefits and potential applications of ITFF with Kalman-integrated covariance Weiner filtering in college music education, including audio signal enhancement, sound synthesis, and interactive performance systems. The integration of computer music technology with advanced filtering techniques presents new opportunities for exploring sound, composition, and music production within an educational context.
Secure Cloud SDN Educational Management with Internet + Learning Management System
Authors: Yang Rui
Published: September 2023

The education management model refers to the system and processes that colleges and universities use to manage and oversee their academic programs and operations. However, with the advent of digital technologies, there has been a growing trend towards the Internet+ college education management model, which integrates digital technologies into all aspects of college education management. This model includes the use of online learning platforms and tools, such as learning management systems (LMS), to deliver courses and manage student progress. It also includes the use of digital technologies for administrative tasks such as admissions, enrolment, and financial aid. However, the educational management model is subjected to the challenge of security for educational data management. Hence, this paper constructed a secure framework model of the Ethereum SDN Cloud Architecture (ESDNarc). The ESDNarc model uses the Software-defined Network (SDN) for the decentralized management of the network, secure transactions, and improved efficiency. The ESDNarch model incorporates the SDN with the cryptography scheme the secure the data. The constructed model uses the double-hashing Elliptical Curve Cryptography (DHECC) for the data stored in the Ethereum blockchain. The performance of the constructed model is evaluated with the KDD data set. Simulation analysis stated that ESDNarch significantly increases the data security in the cloud model for the attacks in the network.
Artificial Intelligence Model Integrated with BIM Model for Core Construction of Transportation Hub
Authors: Wang Lei , Ismail Mazran Bin , Basher Hazril Sherney Bin
Published: September 2023

BIM technology comprises the digital representation of the physical and functional characteristics of the building or structures. It provides the architectural model, engineering, and professional construction with the collaboration of construction projects in an efficient and effective manner. BIM technology can be used to create 3D models of transportation hubs, which can help visualize and simulate different scenarios, optimize space utilization, and improve safety. With detailed 3D models and simulating different scenarios, BIM technology can help optimize space utilization, improve safety, and enhance the overall travel experience for passengers. Core architectural design refers to the fundamental design principles and elements that form the foundation of a building or structure. Hence, this research designed Artificial Intelligence (AI) integrated fuzzy set (AIF-BIM) model for the transportation hub construction. The design of the AIF-BIM model uses the associative rule-based model for the design of the transportation hub. With a designed AI model BIM technology is integrated for the examination of design, construction, and operation. Through the AIF-BIM model, the transportation hub engineering architects are evaluated for the different phases such as the design phase and construction phase. Simulation analysis stated that the application of BIM technology with AIF – BIM in transportation hubs can improve their design, construction, and operation.
Distributed Machine Learning Architecture for Security Improvement in Computer Drafting and Writing in Art Asset Identification System
Authors: Yin Xing
Published: September 2023

Art asset identification service is becoming increasingly important in the art market, where the value of art assets is constantly changing. The service provides authentication, evaluation, and provenance research for artworks, which helps art collectors and institutions to protect their investments and ensure the authenticity of their collections. The effective management of big data is critical for the art asset identification service, and there are several big data management technologies that can be achieved. To improve security in the big data Management model uses Distributed Associative Rule Mining is implemented with Hashing based Symmetric Key Cryptography. The designed model comprises of Associate Rule Hashing Symmetric Key (ARHSK). The proposed ARHSK model comprises the symmetric key generated with the hashing model to secure art assets. With the ARHSK information is stored and processed for security features. The performance of the ARHSK model is implemented with the machine learning model for classification. Simulation analysis expressed that ARHSK exhibits an improved classification accuracy of 99.67% which is ~13% higher than the CNN and ANN models.
Adapting Machine Learning Techniques for Developing Automatic Q&A Interaction Module for Translation Robots based on NLP
Authors: Xu Tao
Published: September 2023

Research on Automatic Q&A Interaction Module of Computer-based Translation Robot is a study that focuses on developing an automatic question and answer (Q&A) interaction module for computer-based translation robots. The goal of the research is to enhance the capability of translation robots to perform more human-like interactions with users, particularly in terms of providing more efficient and accurate translations. In this paper proposed a Conditional Random Field Discriminative Analysis (CRFDA) for feature extraction to derive translation robot with Q&A. The proposed CRFDA model comprises of the discriminative analysis for the CRF model. The estimation CRF model uses the bi-directional classifier for the estimation of the feature vector. Finally, the classification is performed with the voting-based classification model for feature extraction. The performance of the CRFDA model is examined based on the Name Entity (Nes) in the TempVal1 &2 dataset. The extraction is based on the strict and relaxed feature model for the exact match and slight variation. The simulation analysis expressed that proposed CRFDA model achieves a classification accuracy of 91% which is significantly higher than the state-of-art techniques.
Lung Cancer Detection and Classification using Machine Learning Algorithms
Authors: Mishra Ashwani , Gangwar Sanjeev
Published: September 2023

Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by gathering the dataset from the survey and applying machine learning methods such as Support Vector Machine, Nave Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest. With this result, it is revealed that Decision Tree attained the maximum accuracy of 100% as compared to the others.
Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques
Authors: Shankar Sahana P. , Chaudhari Shilpa Shashikant
Published: September 2023

Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC.
Wrong Way Vehicle Detection in Single and Double Lane
Authors: Bharadwaj Rakhi , Billade Ayush , Chenna Srinivas , Chandrapatle Akshat , Chinchalpalle Girish
Published: September 2023

Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use.
Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)
Authors: Srinivas C. , Venkatramulu S. , Chandra Shekar Rao V. , Raghuram B. , Vinay Kumar K . , Pratapagiri Sreenivas
Published: September 2023

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.
Fraud Detection Using Machine Learning and Blockchain
Authors: Gaikwad (Mohite) Vaishali , Meher Kunal , Dass Ryan , Sarah Jonista Athisaya , D’Souza Jeston , Victor Raymun
Published: September 2023

In the 21st century financial fraud is on the rise in many institutions. Newly released Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. The main goal for us is to develop an efficient fraud detection system and utilize blockchain to create a decentralized banking application. Our team has collaborated on various cutting-edge technologies, such as machine learning and blockchain, to create a sophisticated fraud detection system. We have implemented three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, which have been used to improve the accuracy of the model for detecting fraudulent activities. As for fraud aversion, we have used the Blockchain technology which provides a tamper-proof system that can securely record and track financial transactions, ensuring transparency and security. This feature makes it an ideal solution for fraud prevention, as it guarantees that all transactions are legitimate and free from any manipulations. By combining the power of machine learning and blockchain technology, our team is confident in providing an innovative solution that will benefit all stakeholders involved
Design of Multiple Ontology Based Agro Knowledge Mining Model
Authors: Abdullah Azween , Murali E. , S Sreeji , Balusamy Balamurugan , Rajashree S.
Published: September 2023

Farming is regarded as a major industry in India, accounting for 17% of the country's GDP growth. Agriculture employs 60% of the population hence it is considered an important sector in India. The important factors for agriculture are pest management, disease prevention, irrigation management, soil mineral composition, crop management, location, and the season in which the crop is grown. Hence all this information along with the techniques are well known only by the experienced farmers. Hence it is important to create an agro knowledge management system. As a result, this work makes an attempt to develop a multiple ontology-based agro knowledge management system. The designed system consists of agriculture information related to attributes of soil mineral, moisture, season, location, crop type, and temperature. It consists of multiple ontologies such as soil ontology, crop ontology, location ontology, and crop season ontology to provide agronomy knowledge. Soil ontology is premeditated to classify the soil type in a hierarchical order while crop ontology classifies the crop type, location ontology classifies locations suitable for different crop types and finally, crop season ontology classifies the season that is suitable for different crops. A rule base is built to develop the knowledge base and to validate the truthfulness of the knowledge base. Visualization of a knowledge base is carried out for better understanding and decision-making.