bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Effectiveness of Modified Agility Training and Perturbation Training in Osteoarthritis Knee: A Randomized Controlled Trial Investigating Pain, Functionality, and Joint Stability
Authors: Smita Patil Et al. 
Published: January 2024

Introduction: As a modified hinge joint, the knee joint maintains static and dynamic body stability. The patellofemoral and tibiofemoral joints stabilize the knee by joining the femur, tibia, patella, and fibula. Ligamentum Patella and collateral ligaments support. Primary osteoarthritis (OA) is more common in women and thins knee cartilage. Secondary OA can occur from pathology or injury. Proprioception is impaired in knee OA, affecting balance and coordination. Physiotherapy like agility and perturbation exercises improves neuromuscular control. A knee OA study compares traditional physiotherapy with modified agility and perturbation training to improve treatment and symptoms. Understanding risk factors and symptoms helps prevention and treatment.
Material and Methods: Two groups of sixty osteoarthritis (OA) patients—20 men and 40 women—were formed. In addition to standard physiotherapy, Group B got modified agility and perturbation training. Group A received traditional physiotherapy. For three weeks, interventions were provided three times a week. VAS, TUG, and WOMAC were used to assess pain and function.
Result: According to statistical analysis, both groups' pain and functional outcomes improved dramatically. However, Group B—which got modified Agility and Perturbation training—performed better than Group A. The VAS, WOMAC, and TUG mean pre- and post-session values demonstrated significant pain reductions and functional improvements.
Conclusion: This study shows that modified Agility and Perturbation training is helpful for conservative knee OA treatment. Since it significantly reduced pain and improved functional results, the intervention showed promise as a knee OA treatment. More research is needed to determine the long-term effects and widespread use of this technique in various patient populations.
Proficient Approach for Intrusion Detection using Behaviour Profiling Algorithm and Prevention Using Statistical Model in Cloud Networks
Authors: D. Rajagopal Et al. 
Published: January 2024

Objectives:
The objective of the paper is to discuss the proposed dynamic software model to detect and prevent intrusion in the cloud network.
Methods:
The Behavior Profiling Algorithm (BPA) has been used to detect the intrusion in cloud network. For finding the intruder in the network the Event Log Entries and the network Unique Identification Address (UIA) has been fetched from the server and then the collected attribute values have been transferred to prevention module. In the prevention module the dynamic statistical approach model has been used to prevent the network systems and data which are available in the Cloud Network.
Findings:
For testing the proposed model the 100 cloud network systems were taken and based on the loss of packets (in MB) ranges the samples were classified as 0-100, 101-200, 201-300, 301-400, 401-500, 501-600, 601-700 respectively. The range of data loss is assumed to be an interval of 100 Mbps. It is assumed that the higher the data loss ranges, the more data is lost. The mean, variance, and standard deviation were calculated to verify the data loss ranges. The mean (average) of the data loss in the ranges 0-100 is 060.77 and the mean in the ranges 101-200 is 144.714 data losses, which gradually increases in proportion to the data loss ranges, and in the ranges 601-700 it is 665.769 data losses. From the statistical approach model, the differences between mean and variance indicated that the intruder attacked the files during the data transformation in the network. Therefore, the administrator has to monitor the warning message from the proposed IPS model and get data packet losses in the transformation. If the frequency of data loss is low, the administrator can assume that the data flow is low due to network problems. On the other hand, if the frequency of data loss in the network system is high, he can block the transformation and protect the data file. This paper concludes that the behavioral profiling algorithm combined with a statistical model achieves an efficiency of over 96% in wired networks, over 97.6% in wireless networks, and over 98.7% in cloud networks.
Novelty:
In the previous paper discussed the approach which has been implemented with 40 nodes and the result of the proposed algorithm produced above 90%, 96% and 98% in the wired, wireless and cloud network respectively. Now, the model has been implemented with 100 nodes the result has been increased. This study concluded that, the efficient algorithm to detect the intrusion is behaviour profiling algorithm, while join with the statistical approach model, it produces efficient result.
Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction Using Machine Learning Algorithms
Authors: CMM. Mansoor Et al. 
Published: January 2024

Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance.
Characterization of Bismuth Titanate by Line Profile Analysis
Authors: Anchana Joseph Reenu Jacob, 
Published: January 2024

Bismuth Titanate (BIT), Bi4Ti3O12, was synthesized using the solid-state reaction process from the oxide mixture of Bi2O3CO3 and TiO2. The characterization analysis of the prepared sample was done by X-ray diffraction technique. The X-ray diffraction data revealed an orthorhombic perovskite phase that was clearly explained. The Scherrer method, modified W-H analysis, and the SSP studies were conducted to figure out the crystallite size and micro strain in Bi4Ti3O12. Morphological studies included SEM and TEM analysis. FTIR spectral investigation supports the M-O coordination of the synthesized Bi4Ti3O12 sample. The phase formation patterns of Bi4Ti3O12 determined by different methods are highly connected.
GEO based Adaptive Dynamic Key Generation Scheme for Speck-R in IoT Applications
Authors: M. Abinaya Et al. 
Published: January 2024

By integrating lightweight properties with lightweight cryptography in Intenet of Things (IoT) applications, information confidentiality and dependability can be guaranteed. In order to cut down on the number of rounds and speed up execution, a dynamic key-substitution layer was included to the original Speck's structure to create the ultra-lightweight encryption algorithm Speck-R.In this paper, we propose to design Golden Eagle Optimizer (GEO) based adaptive dynamic key generation scheme for Speck-R. In this algorithm, the block size and key size are adaptively chosen based on the IoT application and data size. Then the dynamic key (DK) is generated using GEO algorithm in which a fitness function is derived in terms of the metrics LPF, DPF and BIC. Then the key with optimum fitness is chosen as the dynamic key for further encryption /decryption process of Speck-R. By simulation results, we show that the proposed technique enhances the security.
Novel Classification and Prediction of Heart Disease using CDMA Algorithm
Authors: Tintu George Et al. 
Published: January 2024

Chronic illness is a long-term condition that lasts a lifetime. In most cases, immunizations and medications cannot heal them, or they do not work. The most common chronic illnesses are heart disease. The first step in stopping the progression of these disorders is patient diagnosis and prognosis. The identification of individuals with heart disease may be made easier with the machine learning (ML) and deep learning (DL). Finding people who is at risk for these well-known illnesses is often influenced by a variety of circumstances. High precision is provided by deep learning. Machine learning, however, provides less precision. Deep learning also needs a lot of data. However, machine learning can be trained on less data. By doing so, we may determine that one technique's flaw is fixed by another. To classify and forecast heart disease, this research developed an algorithm by combining ML and DL algorithm that is Combination of Machine Learning and Deep Learning Algorithm (CMDA). The data set for the work was taken from UCI data repository. The CMDA algorithm uses the Dl4jMlpClassifier and the Support Vector Machine (SVM). The technique like stacking classifier is used to integrate above two algorithms in the CMDA. The classification method utilized Naive Bayes as a meta-classifier in the CMDA algorithm, uses a stacking classifier strategy for final prediction. After prediction finally, the CMDA method utilizes the Min-Max normalization approach to determine risk factor. According to the experimental findings, the proposed CMDA algorithm effectively classifies and forecasts heart disease and produce high results while comparing with existing methods.
Snowball-Miner: Integration of Deep Learning for Extraction of Cyber Threat Intelligence from Dark Web
Authors: Abir Dutta Et al. 
Published: January 2024

In Cyber threat intelligence is a crucial component in defending against cybersecurity threats. Cyber security dark web, security Blogs, Hackers’ community, news forums, Open-Source Intelligence (OSINT) are known as the harbor of illicit activities and serve as a breeding ground for cybercriminals. Extracting actionable intelligence from the dark web is challenging due to its anonymous and encrypted nature. State-of-art work proposed machine learning and deep learning approach to aggregate the dark web for cyber threat intelligence from data present in the dark web. This paper proposes, a novel approach utilizing Snowball-Miner for cyber threat intelligence discovery from the dark web. The model is trained on a diverse dataset consisting of dark web forums, hidden .onion based marketplaces and other underground platforms using Snowball-crawler. However, we have employed hybrid convolutional model CNN-LSTM and CNN-GRU adopting doc2vec word embedding to classify into four domains viz Energy Sector, Finance, Illicit Activities and illegal Services. From our experiment it emerged that, CNN-LSTM outperforms as 96.37% for classification of domain specific threat documents. Furthermore, after data preparation we implemented NLP technique and extracted the domain specific Indicator of Compromise (IoCs) using RegEx parser and Subject, Object and Verb (SOV) semantics dependency analysis. Finally, we have integrated IoCs and Threat keywords with respective domains to generate domain specific threat intelligence which enhance the quality of the domain specific CTI based on R-dimension (Relevance).
System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition
Authors: Sarabjeet Singh Et al. 
Published: January 2024

Social recommender systems are getting up more attention for product advertisement and social connectivity. A good recommender should think about the system and the user. The user will have a preference list of some items and these preferences can be useful in suggesting the things which can help the endorsing system to identify better items. In this paper, the idea of social recommender systems as a pattern matching and regular expression making is used for unification of similarities. The concept of mutual profile pattern expression can be applied on various networking platforms. In these type of shared platforms, people all around the globe share resources and interact with each other. In order to manage or scrutinize users according to their interests and likeness, the mutual profile pattern of users can be used. Further predicting of membership function is performed to show how much extent does the profile matches.
An Empirical Performance Analysis of Multi-Classification of Diseases of Tomato Leaf using CNN Models in the Deep Learning
Authors: Sravan Kumar G Et al. 
Published: January 2024

Tomato farming in India, producing tomatoes is one of the leading productions and stands second-largest producer of tomatoes in the world. Tomato farming has been facing challenges as the crop is susceptible to tomato diseases that include Bacterial_Spot, Early_Blight, Septoria_Leaf Spot, Spider_Mites and Late_Blight, that accounts to massive decline in the crop production. The significant drop in the production raises alarm in the analysis of the leaf of tomato with adoption of state of art technologies into the farming. The analysis of tomato leaf with the intent of early prediction of particular disease, includes employment of Convolutional Neural Network (CNN) Models include LetNet5, ResNet50 and AlexNet of the Deep Learning. The proposed work employed the kaggle database tomato leaf diseases dataset that contains 10,000 images that consist of healthy leaves and disease affected leaves. Deep Learning includes Convolutional Neural Networks models: LetNet5, ResNet50, AlexNet are applied on the disease affected and healthy leaves of tomato dataset and it is performed empirical analysis of the CNN models in the prediction of diseases of leaf of tomato through metrics related to performance such as F1-Score, Accuracy, Precision, Recall. The proposed work which highlights empirical performance analysis of the CNN models: LetNet5, ResNet50, AlexNet, provided the noteworthy result that ResNet50 model is able to perform multi-classification the tomato leaf diseases with better accuracy 0.98701 and F1-score 0.98932.
Quantum Machine Learning Technique for Automatic Retrosynthetic Reaction Pathway Search Method
Authors: Jagannath Jijaba Kadam Et al. 
Published: January 2024

Retrosynthetic analysis often involves evaluating many potential candidate reaction pathways and molecules at multiple stages of the reaction, resulting in complex retrosynthesis trees that need to be searched and parsed efficiently. Computational approaches could significantly aid the chemist in solving different aspects of the retrosynthesis problem, such as the graph-theoretic search methodologies for efficient tree traversal to identify feasible reaction pathways, dictionary-based methods to evaluate a large search space of precursors, and chemistry-driven heuristics to eliminate practically infeasible routes. In this research, a new single-step retrosynthesis prediction method of the Retro TRAE SMILES-based translation technique is proposed. Accordingly, quantum computing with tree-tensor network topology is presented to construct an automatic data-driven end-to-end retrosynthetic route planning system (Auto-Syn-Route), which is presented based on the heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The model is trained in an end-to-end and fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, a new way of representing a chemical reaction based on molecular fragments is introduced. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods. The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. The proposed method is implemented using Python software. The proposed approach predicts highly similar reactant molecules with an accuracy of 68%. In addition, the proposed method yields more robust predictions than existing methods. However, the experiments demonstrate that the proposed scheme significantly improves the success rate of solving the retrosynthetic problem by 97% while maintaining the performance of the quantum tree tensor for predicting valid reactions.
Improved Rsa Algorithm for Data Security against DDoS Attack in a Cloud-based Intrusion Detection System
Authors: S. Sujitha Et al. 
Published: January 2024

Today, more and more industries are using cloud computing for some integration operations, but ensuring the security of user data and system resources remains a challenge. This article proposes a method to identify and mitigate unwanted packets and traffic, especially duplicate packets, in cloud computing environments. The method includes creating an Intrusion Search and Detection (IF-AD) system to securely maintain user information and allocate secondary memory. To detect unwanted traffic, this method compares the size of the downloaded file with the original file, identifying any differences as potential DDoS. RSA encryption mechanism is used for subsequent file transfers for added security. The proposed approach aims to enhance the security posture of cloud-based systems by detecting and preventing unauthorized access and file modification.
Self-Improved Pelican optimization for Task Scheduling in Edge Computing: Neural Network based risk probability estimation
Authors: Bantupalli NagaLakshmi Et al. 
Published: January 2024

The aim of edge computing is to drastically speed up the task response time while using less energy. The computational resources in EC are situated in closer proximity to the information generation sources, resulting in lower network latency and bandwidth utilization related to cloud computing (CC). In an EC system, the edge server handles and manages the requests of task and generated information from adjacent IoT machines. The task's schedule is regarded as the optimization problem. Thus, this paper aims to provide a novel task scheduling model that considers Risk probability, Execution cost, Execution time, and Makespan in to account. The Neural Network specifically estimates risk probabilities while taking task security and virtual machine security into consideration. This work suggests a new Paetro distribution-based pelican optimization algorithm (PDPOA) model for optimum scheduling of tasks. Results from the proposed system are examined and compared to existing methods via certain measures including Makespan, Execution time, Execution cost, Risk probability, etc.
Backpropagation Neural Network Adaptive Voltage Control of a High-Gain Transformerless DC/DC Boost Converter for solar applications
Authors: Mohcine Byar, Et al. 
Published: January 2024

An artificial neural network (ANN) adaptive control method for high-gain transformerless DC-DC boost converter is designed in order to achieve better performance and robustness. An ANN controller have the ability to predict the duty cycle in each moment because it is already trained with a vast number of random values of input, output and reference voltages. In order to minimize the error of obtaining the most convenient duty cycle, a back-propagation algorithm has been used to train the neural network because it has the ability to deal with the weights constantly for the sake of diminishing the loss function which makes the ANN generates the fittest duty cycle. The control scheme has been done for a photovoltaic system and the simulation is done using Matlab/Simulink. Simulation results are given for irradiance, reference voltage and load variations. The results are designated to prove the effectiveness of the proposed control system.
Revolutionizing 5G Networks: A Synergy of Routing, Clustering, and Energy Optimization for Unprecedented Performance and Extended Lifespan
Authors: Fathima Rubeena Et al. 
Published: January 2024

The concept of revolutionizing 5G (Fifth Generation) networks through a synergy of routing, clustering, and energy optimization is indeed a promising approach to enhancing the performance and lifespan of wireless networks. Exciting changes will occur in the physical, digital, and biological worlds over the next ten years. Although the needs for Beyond 5G (B5G) are not yet fully understood, an effort has been made to stratify 5G progression and B5G. This work highlights the focus on revolutionizing 5G networks through the integration of routing, clustering, and energy optimization techniques. By combining these methodologies, this research work aims to address the complex challenges in 5G networking, such as efficient data routing, resource allocation, and energy consumption. The objective is to achieve both exceptional performance and an extended lifespan for these networks. The proposed work holds promise for significantly enhancing the capabilities of 5G networks, resulting in improved user experiences, optimized resource utilization, and prolonged network lifespan. In order to completely meet the most stringent 5G standards, such as stratification, or deconstruction into existing technologies, will comprise technology scenarios of 5G evolutions. Wireless sensor networks (WSNs), which offer essential data collecting and monitoring capabilities, are made up entirely of 5G networks. These methods are designed specifically for use in 5G networks to increase the network’s lifespan and overall performance. For 5G networks, routing and clustering techniques from WSNs can be modified and optimized to increase energy efficiency and prolong the network lifetime in 5G networks.
Blockage in Coronary Artery Detection and Quantification in Coronary Angiography
Authors: G.R.Jainish Et al. 
Published: January 2024

The segmentation of the coronary angiography is extremely crucial in computer-aided diagnosis of arterial motion evaluation. It is difficult to create an automated vessel segmentation method using vascular structures because of the wide range of intensities and noise. The suggested method is an unsupervised method that uses coronary angiogram of the heart as a source and in order to get vascular centerlines, segment vessels, and identify heart vein blockages. First, a preprocessing procedure is utilised to enhance and get rid of the image's low frequency noise using morphological filters and a contrast constrained adaptive histogram equalisation. The extraction of the vascular structure is done using a morphological hessian-based method. The wide and narrow vessels are removed using two distinct scales. After that, the vessel's axis of rotation is extracted. In order to find the bifurcation, it employs a branch detection algorithm. Obstructions are located by considering the diameter across the vessel's cross section. The efficiency of the suggested method has been evaluated, as evidenced by testing results on a variety of images, achieving an accuracy of 97.08%.