bluuebunny/crossref_metadata_2025 huggingface dataset:
Text Data Analysis in Chinese Folk Music with Effective Clustering Model toward Feature Identification of Inheritance
Authors: Xu Lizhe
Published: September 2023

Folk music based on big data analysis can provide valuable insights into the history, culture, and evolution of traditional music. By understanding the historical and cultural contexts of folk music, one better appreciate its value and contribute to its continued development and inheritance. Big data analysis can help identify patterns and trends in the performance, distribution, and reception of folk music across time and space. In this paper designed a Weighted Clustering Euclidean Feature (WCEF) model to evaluate folk music on the development of inheritance. Initially, the text data is extracted from folk music for the estimation of features in the big data analysis. Secondly, the WCEF model uses a clustering model for a subset of the folk music dataset with Weighted Non-Negative Matrix Factorization (WNMF). With the clustered model feature extraction is computed with Named Entity Recognition (NER). The NER model uses the Euclidean distance estimation for the computation of features in the folk data analysis. Finally, the WCEF model uses the deep learning model for the classification of inheritance in folk music. The experimental analysis stated that the WCEF model effectively classifies the folk music words and their contribution to inheritance.
IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking
Authors: Verma Pushpendra Kumar , Sharma Vikas , Kumar Prashant , Sharma Shashank , Chaudhary Sachin , Preety Preety
Published: September 2023

Internet of Things based Automatic Attendance Management systems that use Artificial Intelligent cameras and deep learning algorithms can suggestively advance the accuracy and proficiency of class presence following in schools, colleges as well as universities. This technology involves the use of cameras that are placed in classrooms or other areas where attendance needs to be monitored.The cameras are equipped with advanced deep learning algorithms that can detect and recognize students based on their unique facial features. These algorithms use machine learning techniques to analyse images and identify individual faces, even in varying lighting conditions and different angles.The data collected by the cameras is then transmitted to an Intenet of Things based platform, which stores and approach the attendance data in real time. This platform can also be used to generate reports and analytics on attendance, helping teachers and administrators make data driven decisions to improve student performance.
Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications
Authors: Prasanthi Tammineni Shanmukha , Rayavarapu Swarajya Madhuri , Lavanya Yenneti Laxmi , Kumar Gottapu Santosh , Rao Gottapu Sasibhushana
Published: September 2023

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics.
Class Imbalance Reduction and Centroid based Relevant Project Selection for Cross Project Defect Prediction
Authors: Bejjanki Kiran Kumar , Kanchanapally Sai Priyanka , Thota Mahesh Kumar
Published: September 2023

Cross-Project Defect Prediction (CPDP) is the process of predicting defects in a target project using information from other projects. This can assist developers in prioritizing their testing efforts and finding flaws. Transfer Learning (TL) has been frequently used at CPDP to improve prediction performance by reducing the disparity in data distribution between the source and target projects. Software Defect Prediction (SDP) is a common study topic in software engineering that plays a critical role in software quality assurance. To address the cross-project class imbalance problem, Centroid-based PF-SMOTE for Imbalanced data is used. In this paper, we used a Centroid-based PF-SMOTE to balance the datasets and Centroid based relevant data selection for Cross Project Defect Prediction. These methods use the mean of all attributes in a dataset and calculating the difference between mean of all datasets. For experimentation, the open source software defect datasets namely, AEEM, Re-Link, and NASA, are considered.
Foodopedia: A Convolutional Neural Network Based Food Calorie Estimation
Authors: Patil Pramod , Panja Bineeta Bidyut , Mundada Govind Deepak , Nandurkar Monika Abhiman
Published: September 2023

The number of calories consumed determines how healthy a body is in the modern world; therefore, it's important to watch your calorie intake to be healthy. People must keep track of their caloric intake in order to become in shape or maintain a healthy weight. The suggested model uses a deep learning algorithm to offer a novel method of calorie measurement. In the medical area, the estimation of dietary calories is crucial. This measurement is derived from the representation of food in various objects, such as fruits and vegetables. The neural network is used to take this measurement. This technique uses a convolutional neural network to determine the calories in food. An image of food is used as the input for this calculated model. The suggested CNN model uses food object identification to calculate the calorie content of the food. Volume error estimation serves as the primary parameter for the outcome, while calorie error estimation serves as the secondary parameter.
Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings
Authors: Kaushik Priyanka , Singh Rathore Saurabh Pratap , Kaur Pramneet , Kumar Harsh , Tyagi Nancy
Published: September 2023

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency.
Intrusion Detection System using the Hybrid Model of Classification Algorithm and Rule-Based Algorithm
Authors: Kadam Abhijit , Garg Bindu , Gayakwad Milind , Joshi Rahul , Kotecha Ketan
Published: September 2023

Intrusion detection system ID is necessary to secure the system from various intrusions. Analysis of the communication to categorize the data as useful or malicious data is crucial. The cyber security employed using intrusion detection systems should not also cause the extra time to perform the categorization. Nowadays machine learning techniques are used to make the identification of malicious data or an intrusion with the help of classification algorithms. The data set used for experimenting is KDD cup 99. The effect of individual classification algorithms can be improvised with the help of hybrid classification models. This model combines classification algorithms with rule-based algorithms. The blend of classification using machine and human intelligence adds an extra layer of security. An algorithm is validated using precision, recall, F-Measure, and Mean age Precision.
The accuracy of the algorithm is 92.35 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.
A Survey of Algorithms Involved in the Conversion of 2-D Images to 3-D Model
Authors: Upadhye Gopal D. , Kaulage Anant , Suryawanshi Ranjeetsingh S. , Tatiwar Roopal , Unawane Rishikesh , Taware Aditya , Todkar Aryaman
Published: September 2023

Since the advent of machine learning, deep neural networks, and computer graphics, the field of 2D image to 3D model conversion has made tremendous strides. As a result, many algorithms and methods for converting 2D to 3D images have been developed, including SFM, SFS, MVS, and PIFu. Several strategies have been compared, and it was found that each has pros and cons that make it appropriate for particular applications. For instance, SFM is useful for creating realistic 3D models from a collection of pictures, whereas SFS is best for doing so from a single image. While PIFu can create extremely detailed 3D models of human figures from a single image, MVS can manage complicated situations with varied lighting and texture. The method chosen to convert 2D images to 3D ultimately depends on the demands of the application.
Radial Power Distribution System Fault Classification Model Based on ANFIS
Authors: Tiwari Garima , Saini Sanju
Published: September 2023

The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults.
A Trust-Based Group Key Management Protocol for Non-Networks
Authors: Jain Rituraj , Varshney Manish
Published: September 2023

In this paper, a secure and trust-based group key management protocol (GKMP) is presented for non-networks such as MANET/VANET. The scheme provides secure communication for group members in a dynamic network environment and does not restrict the users (registered or non-registered), allowing for flexible group communication. The proposed scheme is designed to address the challenges of key distribution, secure grouping, and secure communication. For result evaluation, first of all formal and informal security analysis was done and then compared with existing protocols. The proposed trust-based GKMP protocol satisfies the authentication, confidentiality of messages, forward/backward security concurrently as well as shows robustness in terms of packet delivery ratio and throughput.
Information Technology and Quality of Service during COVID-19 in a Health Center, ICA 2022
Authors: Yarasca-Meza Mirtha , Uribe-Hernández Yrene , Tello-Aguilar Carmen , Silva-Narvaste Bertha , Ortiz-Gutierrez Jose
Published: September 2023

The use of health information technology allows  improving health care that could not be performed during the pandemic, in addition to controlling patients whose diagnoses are chronic can be treated remotely, so the research objective is to determine the Information Technology and quality of service during COVID-19 in a health center,  Ica 2022. It is a quantitative correlational study, with a total population of 206 workers who filled out a questionnaire with sociodemographic data and information and communication technology instruments; and the scale of quality of care of health services. In the results, 92.2% (n=190) of the participants always frequently use information and communication technologies; and 7.8% (n=16) sometimes use information and communication technologies. In conclusion, workshops should be held to promote the importance of the use of information and communication technologies, in order to improve care processes in the establishment.
Improving the Performance of OLSR in Wireless Networks using Reinforcement Learning Algorithms
Authors: Rani Seema , Charaya Saurabh
Published: September 2023

The Optimized Link State Routing Protocol is a popular proactive routing protocol used in wireless mesh networks. However, like many routing protocols, OLSR can suffer from inefficiencies and suboptimal performance in certain network conditions. To address these issues, researchers have proposed using reinforcement learning algorithms to improve the routing decisions made by OLSR. This paper explores the use of three RL algorithms - Q-Learning, SARSA, and DQN - to improve the performance of OLSR. Each algorithm is described in detail, and their application to OLSR is explained. In particular, the network is represented as a Markov decision process, where each node is a state, and each link between nodes is an action. The reward for taking an action is determined by the quality of the link, and the goal is to maximize the cumulative reward over a sequence of actions. Q-Learning is a simple and effective algorithm that estimates the value of each possible action in a given state. SARSA is a similar algorithm that takes into account the current policy when estimating the value of each action. DQN uses a neural network to approximate the Q-values of each action in a given state, providing more accurate estimates in complex network environments. Overall, all three RL algorithms can be used to improve the routing decisions made by OLSR. This paper provides a comprehensive overview of the application of RL algorithms to OLSR and highlights the potential benefits of using these algorithms to improve the performance of wireless networks.
A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction
Authors: Kavuri Karunakar , Kavitha M
Published: September 2023

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP.
Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization
Authors: Jagtap Sheetal G. , Kunte Ashwini S.
Published: September 2023

It is important to determine the direction of arrival (DoA) of targets in various applications such as radar and sonar. Multiple Signal Classification (MUSIC), Estimation of Signal Parameters with Rotational In variance Technique (ESPRIT), and Weighted Subspace Fitting (WSF) are subspace-based methods that can be used to improve DoA estimation. MUSIC is effective for high-resolution, uncorrelated signals, but may struggle in cases where there are two nearby targets with a low signal-to-noise ratio (SNR). The goal of this research is to improve the performance of the MUSIC algorithm for DoA estimation with low SNR signals. The proposed solution involves decomposing and normalizing the signal during transmission. Simulations were conducted to test the modified procedure with MUSIC algorithm for DoA estimation, and it was found that received signal power improved though there is noisy environment as well as system can detect more number of targets. The proposed technique of decomposition and normalization could also be applied in other areas such as WiFi communication, autonomous vehicles and biomedical signal and image processing etc.
Reduction of Power Losses in the Distribution System by Controlling Tap Changing Transformer using the PSO Algorithm
Authors: Reddy M. Rama Prasad , Kumar Isukapalli Kranthi , Senthilkumar A. , S J Rudresha , Reddy N. Madhu Sudhan
Published: September 2023

Energy is an essential commodity for everyone, with electrical energy being the most preferred form. Unfortunately, non-renewable energy resources are gradually depleting, and renewable energy sources take several years to establish. To mitigate this problem, technology has shifted from non-renewable energy sources to electrical devices and machines, including household appliances like washing machines and air conditioners. However, the generation of electricity is still inadequate to meet the growing demand. This leads to two critical issues: Excessive power loss and inadequate voltage stability, making it difficult for power distribution companies to ensure a consistent and reliable power supply. The objective of this study is to tackle the issue of reduction and minimization of power dissipation By employing the PSO technique, adjusting the transformer tap settings. The proposed approach uses the 14-bus system as a reference and calculates losses for this system using the backward-forward sweeping technique.
An Automated System for Depression Detection Based on Facial and Vocal Features
Authors: Patil Mohit , Khedkar Vijayshri
Published: September 2023

Diagnosing depression is a challenge due to the subjective nature of traditional tools like questionnaires and interviews. Researchers are exploring alternative methods for detecting depression, such as using facial and vocal features. This study investigated the potential of facial and vocal features for depression detection using two datasets: images of facial expressions with emotion labels, and a vocal expression dataset with positive and negative words. Four deep-learning models were evaluated for depression detection from facial expressions, and two traditional machine-learning models were trained for sentiment analysis on the vocal expression dataset. The CNN model performed best for facial expression analysis, while the Naive Bayes model performed best for vocal expression analysis. The models were integrated into a web application for depression analysis, allowing users to upload a video and receive an analysis of their facial and vocal expressions for signs of depression. This study demonstrates the potential of using facial and vocal features for depression detection and provides insight into the performance of different machine learning algorithms for this task. The web application has the potential to be a useful tool for individuals monitoring their mental health and may support mental health professionals in their clinical assessments of depression.
Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management
Authors: Phursule Rajesh , Lal Dhirajkumar , Waghere Sandhya , Mughni Mohammad Abdul , Ransubhe Sarvesh , Shiralkar Chinmay
Published: September 2023

Traffic congestion is a persistent problem in many metropolises worldwide. Despite the existence of traffic control systems, they are not always efficient enough to manage the ever-changing traffic density environment. The traditional approach of allocating specific times to each lane with the green light, regardless of the traffic situation, has not been very effective. In fact, it often can make the traffic congestion worse. Thus, the need for a more sophisticated system has emerged to simulate and optimize traffic control. This paper proposes the use of computer vision technology to develop a traffic control system that is based on periodic still photo feeds and compares different object detection models to find the best model for vehicle detection in our system . The system aims to enhance traffic flow by dynamically adjusting the traffic light cycles based on real-time traffic conditions.
Client Classification and Dynamic Pricing of Resources in Cloud using Cloud Simulator
Authors: Ponnuswamy P.  Priya , Shabariram C. P.
Published: September 2023

In cloud computing scenarios, the resources like hardware or software or applications can be accessed whenever a client needs. The client needs to get a subscription to avail service. The usages are monitored by service provider for each client over a network. When more number of client issues a request in cloud, scheduling the resources and pricing of taken resources from the cloud have a huge difference on the incomes. The job request from the user is  scheduled in cloud is done, based on the need of the client (i.e) client classification algorithm and based on the demand pricing is fixed (i.e) dynamic pricing scheme. These algorithms solves income optimization problem by allocating resources under a given price and pricing of the resources are based on the allocation. The experiments were proposed with cloud simulation. Cloud simulations results are measured with the first come first serve, client classification and dynamic pricing model. The proposed algorithm shows the better resource allocation based on the pricing method.
Multi-Scale Hybrid Spectral Network for Feature Learning and Hyperspectral Image Classification
Authors: Kondal Easala Ravi , Barpanda Soubhagya Sankar
Published: September 2023

Hyperspectral image (HSI) classification is an important concern in remote sensing, but it is complex since few numbers of labelled training samples and the high-dimensional space with many spectral bands. Hence, it is essential to develop a more efficient neural network architecture to improve performance in the HSI classification task. Deep learning models are contemporary techniques for pixel-based hyperspectral image (HSI) classification. Deep feature extraction from both spatial and spectral channels has led to high classification accuracy. Meanwhile, the effectiveness of these spatial-spectral methods relies on the spatial dimension of every patch, and there is no feasible method to determine the best spatial dimension to take into consideration. It makes better sense to retrieve spatial properties through examination at different neighborhood scales in spatial dimensions. In this context, this paper presents a multi-scale hybrid spectral convolutional neural network (MS-HybSN) model that uses three distinct multi-scale spectral-spatial patches to pull out properties in spectral and spatial domains. The presented deep learning framework uses three patches of different sizes in spatial dimension to find these possible features. The process of Hybrid convolution operation (3D-2D) is done on each selected patch and is repeated throughout the image. To assess the effectiveness of the presented model, three benchmark datasets that are openly accessible (Pavia University, Indian Pines, and Salinas) and new Indian datasets (Ahmedabad-1 and Ahmedabad-2) are being used in experimental studies. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification performance.
FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment
Authors: Vadivu V. , Kavitha N.
Published: September 2023

— The Information Extraction (IE) approach extracts useful data from unstructured and semi-structured data. Big Data, with its rising volume of multidimensional unstructured data, provides new tools for IE. Traditional Information Extraction (IE) systems are incapable of appropriately handling this massive flood of unstructured data. The processing capability of current IE systems must be enhanced because to the amount and variety of Big Data. Existing IE techniques for data preparation, extraction, and transformation, as well as representations of massive amounts of multidimensional, unstructured data, must be evaluated in terms of their capabilities and limits. The proposed FVI-BD Framework for IOT device Information Extraction in Big Data. The unstructured data has cleaned and integration using POS tagging and similarity finding using LTA method. The features are extracted using TF and IDF. The Information extracted using NLP with WordNet. The classification has done with FVI algorithm.  This research paper discovered that vast data analytics may be enhanced by extracting document feature terms with synonymous similarity and increasing IE accuracy.
Optimization Techniques For Low Energy Consumption In Green Cloud Computing
Authors: Sharma Himanshu , Joshi Vijay Kumar
Published: September 2023

Computing in the cloud can assist businesses in shifting their focus to the development of solid business applications that will bring about genuine value to the businesses. Green computing, often known as environmentally sustainable computing, is the definition of green computing. It is a reference to the efforts that are made to maximise the usage of power consumption & energy efficiency while simultaneously minimising the cost & the amount of CO2 emission. To conduct a study on optimisation techniques & procedures that assist us in optimising low energy consumption & evaluating multiple parameters in order to obtain the desired output is the primary purpose of this research. Energy-Conscious Multisite Computation Offloading Techniques (EMOGC) for Green Cloud Computing is the methodology that was utilised throughout this project. Simulation & analysis are presented in The Energy-Conscious Multisite Computation Offloading Techniques for Green Cloud Computing in order to investigate time-efficient scheduling on multisite, which is responsible for optimising energy, time, & cost at the optimum time. This strategy seeks to finish the application within the allotted amount of time while also consuming as little power as feasible from the connected devices. According to the findings of this research, it is clear that the explored technique is effective in obtaining high throughput (HT) while simultaneously minimising the execution time, which in turn enhances the data rate in Green Cloud Computing (GCC).
Designing of an AI-Based Automated Feedback Assessment and Decision Analytics Model in Virtual Learning Environments using Google BERT
Authors: Srivastav Gaurav , Kant Shri , Srivastava Durgesh
Published: September 2023

Globally, the COVID-19 pandemic has an impact on educational practices. Social isolation regulations have caused schools to close, and educational institutions have shifted to distance or online learning. Both students and teachers have had a hard time adjusting to this adjustment. While instructors battle to design and provide online courses that are acceptable for students, learners may struggle to adapt to new technologies and lack of resources. The admissions procedure, exam times, and academic calendars have all been delayed as a result of the epidemic. To find out how these changes might impact student performance and educational outcomes, research is now being conducted. But it has been noticed that pandemic has raised awareness of the need for increased funding for teacher preparation programmes and digital infrastructure to support distance learning. This study describes the creation of an automated feedback assessment model that makes use of Google's Bidirectional Encoder Representations from Transformers (BERT) to produce a score based on the calibre of input in a virtual learning environment (VLE) during a pandemic. The dataset used for the research consisted of 10,000 feedback statements labelled as either "good" or "bad," and the model was fine-tuned on the Open University Learning Analytics (OULA) dataset and trained for 50 epochs. The model's accuracy on the validation set was 93.4%, demonstrating that it can accurately assess the calibre of feedback remarks. The model has the potential for various applications, such as in education, performance evaluation, and customer service, as it can reduce the time and subjectivity associated with human evaluation.
ACO based Clinical Decision Support System for Better Medical Care
Authors: Anadani Ishwa , Sharma Pavi , Sharma Anand
Published: September 2023

In the realm of healthcare, the utilization of clinical decision support systems (CDSSs) has become increasingly prevalent as a means of providing medical professionals with a computer-based tool that grants them access to pertinent data and expertise, thereby aiding in their ability to make informed clinical decisions. The potential applications of a CDSS are numerous, ranging from disease diagnosis and the creation of treatment programs, to patient progress monitoring. A crucial component of a CDSS is its knowledge base, which comprises the data utilized by the system to generate recommendations and provide feedback to healthcare providers. In an effort to enhance the knowledge base of a CDSS for a particular clinical condition, metaheuristic methods such as Ant Colony Optimization (ACO) can be employed to select the most suitable and applicable data. ACO facilitates the identification of the portion of a CDSS's knowledge base that is most likely to result in the optimal clinical decision, from among the vast array of data that it may contain. This study aims to explore the potential benefits of utilizing ACO methods in CDSSs for the betterment of patient care. The paper outlines the design and implementation of an ACO-based CDSS, which can offer tailored treatment plans for patients based on their medical histories and current condition.
An Analytical Performance Evaluation on Multiview Clustering Approaches
Authors: Mankar Jyoti R. , Kamalapur S. M.
Published: September 2023

The concept of machine learning encompasses a wide variety of different approaches, one of which is called clustering. The data points are grouped together in this approach to the problem. Using a clustering method, it is feasible, given a collection of data points, to classify each data point as belonging to a specific group. This can be done if the algorithm is given the collection of data points. In theory, data points that constitute the same group ought to have attributes and characteristics that are equivalent to one another, however data points that belong to other groups ought to have properties and characteristics that are very different from one another. The generation of multiview data is made possible by recent developments in information collecting technologies. The data were collected from à variety of sources and were analysed using a variety of perspectives. The data in question are what are known as multiview data. On a single view, the conventional clustering algorithms are applied. In spite of this, real-world data are complicated and can be clustered in a variety of different ways, depending on how the data are interpreted. In practise, the real-world data are messy. In recent years, Multiview Clustering, often known as MVC, has garnered an increasing amount of attention due to its goal of utilising complimentary and consensus information derived from different points of view. On the other hand, the vast majority of the systems that are currently available only enable the single-clustering scenario, whereby only makes utilization of a single cluster to split the data. This is the case since there is only one cluster accessible. In light of this, it is absolutely necessary to carry out investigation on the multiview data format. The study work is centred on multiview clustering and how well it performs compared to these other strategies.
LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains
Authors: Pandey Anil Kumar , Misal Ashish Kumar
Published: September 2023

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases.
Effective Workflow Scheduling in Cloud using Constriction Factor based Inertia Weight Particle Swarm Optimization
Authors: Sriperambuduri Vinay Kumar , M Nagaratna
Published: September 2023

Cloud computing allows rapid provision of resources based on the need. This enables users to execute the independent tasks and dependent tasks called workflows on the cloud system. Workflow scheduling is a crucial problem that is NP Hard and is still a challenging problem. Particle Swarm Optimization (PSO) is one of the commonly used metaheuristic algorithms for solving task scheduling problems, but it has issues with premature convergence and lack of diversity. In recent years, chaotic maps have been employed in PSO to enhance its performance. This study proposes a Constriction factor-based inertia weight in PSO for workflow scheduling (CFPSO). The proposed algorithm utilizes a constriction factor for updating the inertia weight, which enhances the exploration ability of the algorithm thereby avoid local optima. The algorithm considers a fitness function with an aim to minimize makespan, service cost, and maximize load balance. The proposed algorithm is evaluated using a set of benchmark workflows, and the obtained results are compared with the standard PSO algorithm, Grey Wolf Optimizer (GWO) algorithm and Chaotic PSO algorithm. The extensive experimentation performed show that the proposed algorithm outperforms the other algorithms in terms of makespan, service cost, and load balance. The proposed CFPSO shows reduction of 20% of makespan, 2% of the service cost and 18% load balance rate compared to the conventional algorithms on Montage workflow with 1000 tasks. The use of constriction factor enhances the performance of the algorithm and makes it suitable for solving complex problems with multiple objectives. The proposed algorithm can be used in real-world applications to optimize workflow scheduling in cloud computing environments.
Machine Learning Based Twitter Sentiment Analysis and User Influence
Authors: Krishna Ragini , C.M. Prashanth
Published: September 2023

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant  in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral.

In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic.

The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy.

The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively.
Deep Convolutional Attention based Bidirectional Recurrent Neural Network for Measuring Correlated Colour Temperature from RGB images
Authors: Mane Sanket Satish , Santra Ajit Kumar
Published: September 2023

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively.
Design and Develop Efficient Arbitration Technique to Handle the Multiple Refresh Requests in Multi-Processor SoC
Authors: Shravan Chintam , Fatima Kaleem , Sekhar Paidimarry Chandra
Published: September 2023

Emerging memory technologies, such as Gain Cell-embedded Dynamic Random Access Memory (GC-eDRAM), play an essential part in the process of improving the overall performance of current multi-processor systems. GC-eDRAM, on the other hand, has its own set of distinct issues, particularly with regard to refresh operations. The number of cores and threads in contemporary processors continues to expand, which in turn leads to an increase in the number of concurrent refresh requests. This might cause contention, which in turn can lead to a possible performance decrease. In this article, we present an efficient arbitration method that was developed in order to precisely address the issues that are associated with numerous requests for a refresh in GC-eDRAM. This method takes use of the inherent parallelism of GC-eDRAM modules to make it possible to execute simultaneous refresh operations. As a result, contention is effectively reduced, and the overall performance of the system is improved. We provide a new arbitration method that prioritizes the pending refresh requests according to their level of urgency and optimizes the allocation of GC-eDRAM resources in order to guarantee that refresh operations are carried out in an effective manner. Our method modifies the arbitration priority in a dynamic manner according to the characteristics of the active workload. These characteristics include the request arrival rate, memory access patterns, and data location, among other considerations.
Quality Analysis of Software Applications using Software Reliability Growth Models and Deep Learning Models
Authors: Sujitha M. Jeevana , Subrahmanyam Kodukula
Published: September 2023

Finding the faults in the software is a very tedious task. Many software companies are trying to develop high-quality software which is having no faults. It is very important to analyze the errors, faults, and bugs in software development. Software reliability growth models (SRGM's) are used to help the software industries to create quality software products. Quality is the software metric that is used to analyze the performance of the software product. The software product which is having no errors or faults is considered the best software product. SRGM is also utilized to analyze the software quality based on the programming language. Deep Learning (DL) is a sub-domain in machine learning to solve several complex issues in software development. Finding accurate patterns from software faults is a very tedious task. DL algorithm performs better in integrating the SRGM with the DL approaches giving better results based on software fault detection. Many software faults real-time datasets are available to analyze the DL approaches. The performances of the various integrated models are analyzed by showing the quality metrics.
Peer-to-Peer Conferencing using Blockchain, WebRTC and SIP
Authors: Meher Kunal , More Gandharv , Singh Aanshi , Fernandes Danyl , Philip Mathew
Published: September 2023

The owner of the centralized video platform has more control over uploaded content than the content producer does. But the other Blockchain-based decentralized video services are attempting to reduce ad pressure and get rid of middlemen. The article suggests a combination of a safe encryption technique and an access control mechanism created "with technology" to create a successful decentralized video streaming platform built on the Blockchain. Peer-to-peer (P2P) overlays are one of the complicated network applications and services that have been migrated to the Web as a result of the increasing support for Web Real-Time Communication (WebRTC) standard in modern browsers for real-time communications. The expansion of access networks’ bandwidth also makes it possible for end users to start their own content businesses. This paper presents a preliminary proposal of metrics and technologies to move toward a decentralized cooperative architecture for large-scale, real-time live stream content de- livery based on WebRTC, without the requirement of a Content Delivery Network (CDN) infrastructure. The paper takes into account the light of the aforementioned aspects [6].
Challenges on Missing Packet Detection or Packet Dropping Attacks in Mobile Adhoc Network -A Survey
Authors: Hemalatha S. , Pallathadka Harikumar , Chinhewadi Rajesh P. , Kalaiyarasi D. , Princy B. Anni
Published: September 2023

Collection of wireless nodes forms together to communicate each other in the network without infrastructure less and any access point along with the characteristics of freedom in mobility is called Mobile Adhoc Network. Transmitting of packets from source to destination plays the vital role in MANET. When a Packet is not delivered properly at the destination , its affects the performance of the MANET.  Due to this characteristics of the mobility nodes can subject to falls on the packet missing also the various packets dropping security attacks take part in the dropping the packets while communication to achieve the decreasing the performance of the MANET. This article focus on the survey about the missing packet assembly as well the packet dropping security attacks in MANET.