bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning
Authors: Mohd Yousuf Et al. 
Published: December 2023

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement. Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education.
Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data
Authors: Vinston Raja R Et al. 
Published: December 2023

COVID disease plague of 2019 (COVID19) has made an overall health related crisis with a very high gamble of spreading and influencing the whole planet. In essentially every nation, new cases have been accounted. To identify all countries expanding number of tests, the manual clustering of COVID-19 and clinical infection information tests becomes tedious and requires profoundly talented work. As of late, a few calculations have been utilized for clustering clinical datasets deterministically; nonetheless, these definitions have not been powerful in gathering and investigating clinical infections. To rank and score more than 200 nations as indicated by COVID-19 cases and casualty in 2020 and contrast the outcomes with existing pandemic weakness forecast models and results produced by standard Data clustering scoring methods. Information clustering is a course of orchestrating comparative information into gatherings. A clustering algorithm bundles an informational collection into a few several clusters such an extent that the similitude inside a gathering is better compared to among clusters. This paper propose new Hybrid clustering algorithm KMHC in view of K-Means and Hierarchical Clustering calculation. This calculation KMHC, First isolated into every nation gatherings or fragments in light of the COVID patients count, Secondly grouping models has been made across the nations on the planet and across the states in India, and the presentation investigation is analyzed. This paper likewise center figures the future COVID count for India. By utilizing this outcome, set of nations which are having higher COVID count can be effortlessly pictured and the proper moves will be made to diminish the count.
A SIW Based Leaky Wave Antenna for Aerospace Applications in X Band
Authors: Ramandeep Kaur Et al. 
Published: December 2023

In the rapidly advancing field of aerospace communication and radar systems, the demand for high performance antennas operating in the X-band frequency range has increased significantly. For high speed communication, precise navigation, and accurate detection the antenna in X band is required. In this paper, the design and analysis of a substrate integrated waveguide (SIW)-based antenna for aerospace applications operating at a frequency of 10.5 GHz. The proposed antenna design integrates the benefits of SIW technology, such as low loss, compact size, and compatibility with planar fabrication techniques, making it suitable for space-constrained aerospace platforms. The antenna had gain of 9.34 dBi, s11 of design is -28.3 at 10.5 GHz. The radiation pattern is approximately omnidirectional.
A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions
Authors: Sahana V Et al. 
Published: December 2023

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media.
Comparison of Different Machine Learning and Self-Learning Methods for Predicting Obesity on Generalized and Gender-Segregated Data
Authors: Khushi Joshi Et al. 
Published: December 2023

Obesity is a global health concern with long-term implications. Our research applies numerous Machine Learning models consisting of Random Forest model, XGBT(Extreme Gradient Boosting) model, Decision Tree model, k-Nearest Neighbors technique, Support Vector Machine model, Linear Regression model, Naïve Bayes classifier and a neural network named Multilayer Perceptron on an obesity dataset so that we can predict obesity and reduce it. The models are evaluated on recall, accuracy, F1-score, and precision. The findings reveal the performance of the algorithms on generalised and gender-segregated data providing insights concerning feature selection and early obesity identification. This research aims to demonstrate the comparative study of obesity prediction for gender-neutral and gender-specific datasets.
An Enhanced Deep Learning Model for Brain Tumor Prediction
Authors: Yeligeti Raju Et al. 
Published: December 2023

Brain tumour diagnosis & prediction is an challenging issue and important area of research. perversely, convolutional neural networks can support this (CNNs). They have mastered computer vision problems as well as other issues like segmenting, detecting, and recognizing visual objects. By enhancing the brain images with help of segmentation methods that are extremely challenging related to noise and cluster size sensitivity issues, as well as automated region of Interest detection (ROI), it helps with the diagnosis of brain tumours. The reality that CNNs have achieved high level of accuracy and it does not require manual extraction of features. Finding a brain tumour and correctly classifying it are challenging tasks. CNN outperforms rivals due to its extensive use in image recognition. Brain tumour segmentation is the most significant and challenging problems in the field of medical image processing research because human assisted manual categorization may lead to inaccurate prediction and diagnosis. In addition, when there is a huge amount of data existing to support in the process, it is challenging. Extraction of tumour areas from images becomes challenging due to the wide variety of appearances of brain tumours and the similarity of tumour and normal tissues.
Impact of Docker Container Virtualization On Wireless Mesh Network by Using Software-Defined Network
Authors: Sanjay Shahaji Kadam Et al. 
Published: December 2023

In today’s advanced digital world era, it is extremely difficult for small enterprises or organizations to merge traditional or legacy computer network devices/equipment and wireless mesh networking devices with the latest digital computer network technology with respect to the expense of buying and maintaining expensive branded networking devices. However, today, by applying the neatly Software-defined networking, the OpenFlow protocol along with virtualization such as docker containers, which is a pack of their specific libraries, configured files, and software, provides advantages over proprietary or branded computer networking devices with respect to purchasing expenditure, operational expenditure, and improved performance in computer networking. Redistribution of routing protocol is very essential when using various autonomous systems in wireless mesh networks. Docker containers of frr and quagga give an edge over traditional or branded physical router devices, some docker containers are used as wired and wireless hosts/clients in the wireless mesh network. The novel idea used in this paper is on how to use the different software-defined controllers (Ryu and Pox controller) in a docker containerized wireless mesh network to analyse with respect to packet transfer, jitter in transmission, minimum delay in transmission, maximum delay in transmission, the average delay in transmission, delay standard deviation bit-rate, send packets, average packets drop, dropped packets along-with average loss-burst size in Mininet Wi-Fi testbed at the different scenario and the result shows that by using the docker container virtualization along with software-defined network two different controllers improves the performance and optimize the wireless mesh network. In addition, it shows that by using containerization and virtualization, capital expenditure and operational expenditure can be reduced in designing and developing wireless mesh network topologies.
Integration of MFCC Extraction and LSTM Algorithm on PYNQ-Z2 for Enhanced Audio Analysis
Authors: Sheetal U. Bhandari Et al. 
Published: December 2023

The need for Speech Emotion Recognition (SER) is growing since researchers have found it difficult to interpret human emotions from speech data. SER is very interesting yet very challenging task of human-computer interaction (HCI). The SER application can be benefitted depending on the type of feature extraction technique and model used for classification. Deep Learning has made a great impact in the field of audio, image, video, EEG and ECG classification. The speech signal characteristics and classification model affect how well the SER application performs. The paper briefs about deploying Deep Learning Algorithm on FPGA based board i.e., PYNQ-Z2. MFCC feature extraction technique and LSTM model used for classification of human emotion is implemented on the board. Emotion can be predicted using led buttons on the board.
Healthcare Record Sharing and Management using Blockchain
Authors: Dhanamma Jagli Et al. 
Published: December 2023

We experience various health problems due to the hectic pace of life, which includes daily hustle and work life, and the deteriorating ecosystem. There are numerous health records produced and every individual has to maintain these many important pieces of private information. Health records are extremely sensitive information that must typically be shared with peers for numerous examinations in the medical field. Healthcare records are managed manually in the current scenario, which raises efficiency, security, and other concerns. This paper puts forward a web application that uses blockchain technology to offer a simple method for securely sharing private medical records. It offers real-time tracking of medical records, enhancing security, boosting productivity, and lowering total costs. By enabling infrequent knowledge transfer between healthcare professionals, insurance agencies, pharmaceuticals, scientists, and caregivers of the patient, it will streamline data exchange, enhance decision-making for healthcare, and ensure availability and genuineness. Doctors could use the application to review the healthcare data of the patients by whom they have been granted access, while individuals could use it to upload their data, grant access to the doctors, and read their own records.
Enhancing Network Security Through Blockchain Technology: Challenges And Opportunities
Authors: Salwa Shakir Mahmood Et al. 
Published: December 2023

The rapid proliferation of digital technologies has ushered in an era where network security is of paramount importance. Traditional security mechanisms have proven insufficient in protecting sensitive data and critical infrastructure from an ever-evolving landscape of cyber threats. Blockchain technology, originally designed to underpin cryptocurrencies like Bitcoin, has emerged as a promising solution for enhancing network security. Blockchain's core principles of decentralization, immutability, and transparency offer a unique approach to addressing vulnerabilities and mitigating risks in the digital realm. This paper examines how blockchain can enhance data integrity, authentication, and authorization processes, thereby fortifying the security posture of networks and systems.
The paper discusses real-world applications and case studies where blockchain has been successfully implemented to bolster network security, such as supply chain management, identity verification, and secure communication protocols. These examples highlight the tangible benefits and opportunities that blockchain presents for organizations seeking to safeguard their digital assets and operations. In conclusion, this abstract underscore the pivotal role that blockchain technology can play in enhancing network security. By addressing challenges head-on and capitalizing on the opportunities it offers, organizations can build resilient, transparent, and secure digital ecosystems that protect against an ever-increasing array of cyber threats. The exploration of blockchain's potential in this context is critical for shaping the future of network security in our interconnected world.
Network Intrusion Detection Using Autoencode Neural Network
Authors: Zakiya Manzoor Khan Et al. 
Published: December 2023

In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy.
A Study on Data Protection in Cloud Environment
Authors: S Arvind Et al. 
Published: December 2023

Data protection in the online environment pertains to the safeguarding of sensitive or important data kept, analyzed, or sent in cloud-based systems. It entails assuring data confidentiality, integrity, and availability, as well as adhering to appropriate data protection requirements. In a nutshell, cloud data protection seeks to protect data against unauthorized access, deletion, or breaches while retaining its accuracy and accessible to authorized users. This is accomplished in the cloud environment using various security measures, encryption approaches, access controls, disaster recovery and backup processes, and constant monitoring and threat detection. The research significance of data protection in the cloud environment can be summarized as follows: Security and Privacy: Research in data protection in the cloud helps address the security and privacy concerns associated with storing and processing sensitive data in cloud-based systems. It explores and develops advanced security mechanisms, encryption techniques, and access controls to protect data from unauthorized access, data breaches, and privacy violations. Trust and Confidence: Research in data protection contributes to building trust and confidence in cloud computing. By developing robust security solutions and demonstrating their effectiveness, research helps alleviate concerns about data security and privacy, fostering greater adoption of cloud services by organizations and individuals. Compliance and Regulations: Cloud computing often involves compliance with data protection regulations and industry standards. Research in this area explores the legal and regulatory aspects of data protection in the cloud and helps organizations understand and comply with relevant requirements. Data Resilience and Recovery: Research in data protection focuses on ensuring data resilience and developing efficient data recovery mechanisms in the cloud. It explores backup and disaster recovery strategies, data replication techniques, and data loss prevention methods to minimize downtime, recover data promptly, and maintain business continuity in the event of system failures or disasters. By addressing these research areas, studies on data protection in the cloud environment contribute to enhancing security, privacy, compliance, and resilience in cloud computing. They provide valuable insights, practical solutions, and guidelines for organizations and service providers to protect data effectively and maintain the trust of users in cloud-based services. The weighted product method approach is commonly used to choose the best data protection in cloud environment. CCSS1, CCSS2, CCSS3, CCSS4, CCSS5 data visibility, data integrity, Maintains compliance, Data security, Data storage. From the result it is seen that CCSS2 got highest rank whereas CCSS5 got lowest rank According to the results, CCSS2 was ranked first.
Design Optimization of Solar Power Inverter
Authors: Sridhar P Et al. 
Published: December 2023

Optimizing the design of solar power inverters aims to improve efficiency, dependability, and performance. Effective circuit design, component selection, and advanced power electronics design are all involved. Effective methods for trouble identification and heat control guarantee long-term dependability. Complex control algorithms and filtering techniques enable the generation of high-quality AC output. Different system sizes can be integrated thanks to scalability. Monitoring and communication features enable remote control and real-time performance monitoring. Safety features adhere to all relevant standards and laws. For the highest return on investment, cost-effective design selections balance performance and affordability. Check to see if any specific aspects, such as dependability, power quality, scalability, cost-effectiveness, or efficiency, need to be improved. data collection Collect all necessary data, such as details on solar panels, load specifications, grid standards, environmental factors, and any other details that might affect the design. Modelling and simulation an electronic or mathematical model of the solar power inverter system should be created. This model should be able to accurately represent the inverter's electrical properties, control structures, and operating behavior. Utilise simulation tools to assess the system's performance under various conditions. Utilizing optimization techniques, algorithms are used to discover the optimal design solution. These algorithms could be based on methods for machine learning, evolutionary algorithms, or mathematical optimization methods. Define the objective function and constraints of the optimization process. Iterative Refinement for Modify the design in response to the optimization's results. Iterate through the design exploration, performance evaluation, and optimization stages to reach a superior design solution. The parameters of the model are continuously updated and modified as required. Taken as alternate parameter is Air Conditioner, Fan, Iron Box, Laptop Charger, Microwave Oven, UPS, Cooler. Taken as evaluation parameter is Battery, Filter capacitor (C1), Filter inductor (L1), Switching Frequency, DC link Capacitor (C2) 2 nos.
An Efficient Machine Learning Approach for Prediction of Conjunctiva Hyperemia Assessment using Feature Extraction Methods
Authors: Savita Bamal Et al. 
Published: December 2023

The human eye is one of the most intricate sense organs. It is crucial to protect your eyes against several eye disorders that can cause vision loss if untreated in order to maintain your ability to see well. Early detection of eye diseases is therefore crucial in order to prevent any unintended consequences and control the diseases continued progression. Conjunctivitis is one such eye condition that is characterized by conjunctival inflammation, resulting in symptoms like hyperemia (redness) due to increased blood flow. With the aid of the best treatments, modern techniques, and early, precise diagnosis by professionals, it can be cured or can be greatly reduced. The proper diagnosis of the underlying cause of visual problems is frequently postponed or never carried out because of shortage of diagnostic experts, which leads to either insufficient or postponed corrective treatment. In order to diagnose and evaluate conjunctivitis, segmentation methods are essential for locating and measuring hyperemic regions. In the present study, segmentation techniques are applied along with feature extraction techniques to provide an effective machine learning framework for the prediction of eye problems. Using the discrete cosine transform (DCT), the segmented regions of interest are converted into feature vectors. These feature vectors are then used to train machine learning classifiers, including random forest and neural networks, which achieve a promising accuracy of 95.92%. This approach enables ophthalmologists to make more objective and accurate assessments, aiding in disease severity evaluation.