bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Personalized User Interfaces of Intelligent Training Systems Based on Neural Network Technologies
Authors:   Federal State Budgetary Educational institution of higher education ", Tagirova L. F. ,   Orenburg State University",   , Orenburg, Russian Federation, Semenova N. G. ,   Federal State Budgetary Educational institution of higher education "Orenburg State University", Orenburg, Russian Federation
Published: September 2023

The article is devoted to the problem of developing adaptive interfaces of application programs, taking into account the individual characteristics of users. The novelty of the study is the use of two types of artificial neural networks (INS), which implement the creation of a personalized prototype of the interface, depending on the individual characteristics of the user. The first is a convolutional INS used to assess gender and age characteristics, as well as the emotional state of the user, based on recognition of his face. Second, deep INS is used to select the menu components of the adaptive interface prototype. The results of the experimental operation of the developed intelligent training system (IOS) with an adaptive interface showed an increase in the quality and efficiency of students' work when studying the material presented in the IOS.
Intelligent System for Recognition of Road Infrastructure Objects Using a Neural Network Model
Authors:   Kazan National Research Technical University named after A. N. Tupolev, Khusainov R. M. , Talipov N. G. ,   Kazan National Research Technical University named after A. N. Tupolev, Katasev A. S. ,   Kazan National Research Technical University named after A. N. Tupolev
Published: September 2023

The article deals with the problem of recognition of road infrastructure objects. The expediency of solving this problem using an intelligent system based on a neural network model is updated. In order to develop it to solve the problem, the collection of initial data, the choice of libraries, the construction of the architecture and the training of the neural network were carried out. The initial data for constructing the model was obtained when generating queries in a search engine and presented in the form of images of traffic signs, road markings and traffic lights. To build a neural network, training and test samples were formed for 3 classes of images, taking into account various road conditions. When building a neural network model, the activation functions Relu and Softmax were used. The neural network model includes 4 convolution layers, 2 subsampling layers and 2 fully connected layers. The IDLE development environment was used to train the model. The program code is written in the Python 3.9.7 programming language. The neural network model was trained for 10 epochs. An example of the operation of the system and the results of testing the developed built model are presented. Studies have shown that the developed intelligent system and neural network model for recognizing road infrastructure objects are adequate, and its accuracy is 92 %. The achieved accuracy of the neural network model is not inferior to the accuracy of other methods for classifying objects in images.
Information System for Evaluation of Operator's Preparation for Control Conditions of Ergacy System without Feedback
Authors:   V. A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, 117997, Russian Federation, Turovsky Ya. A. , Alekseev V. Yu. ,   Voronezh State University, Voronezh, 394018, Russian Federation, Borzunov S. V. ,   Voronezh State University, Voronezh, 394018, Russian Federation
Published: September 2023

A description of an information system is presented that provides an assessment of the ability of the «interface-operator» complex to form control commands for an ergatic system in the absence of visual control of the control process
Housing Pathways of the “Missing People” of Public Housing and Resettlement Programs: Methodological Reflections
Authors: Beier Raffael
Published: September 2023

This article deals with methodological challenges and presents solutions for the study of people who depart from state-subsidized housing in Ethiopia, Morocco, and South Africa. Having sold or rented out their units, these people have left and now live at dispersed locations. Assuming that many “missing people” leave state housing because of project-related shortcomings, studying the reasons for their departure is crucial to understanding standardized housing programs. “Missing people” urge scholars to emphasize the afterlives of housing policy interventions as a necessary analytical dimension. However, such research is confronted with three major methodological challenges: How is it possible to approach and study people who have disappeared from the area of a housing intervention? How can one link exploratory, in-depth qualitative accounts, rooted in subjective perceptions of the everyday, to potential structural deficiencies of standardized housing interventions? What kind of methodologies may help take into account the temporalities of displacement and resettlement? In order to overcome these challenges, the article presents innovative forms of purposive sampling and discusses analytical strategies, which—based on Clapham’s framework of “housing pathways”—bridge relational and structural perspectives to housing programs.
THE FACTORS OF INFRAHUMANIZATION OF RUSSIANS AND BELARUSIANS IN UKRAINIANS' PERCEPTION
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Bondar O. M. , Vinogradov A. G. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Published: September 2023

This paper delves into the complexities of intergroup dynamics, specifically focusing on how Ukrainians perceive citizens of countries implicated in largescale invasion, namely Russia and Belarus. The research seeks to illuminate the subjective experiences of Ukrainians in terms of primary and secondary emotions, and further quantifies the extent of infrahumanization--a subtler form of dehumanization--towards these out-groups. The theoretical section of the study elaborates on the concept of infrahumanization and its significance in the context of group identification, differential group status, and the legitimacy and stability of these factors. It provides an insightful understanding of how group identities are formed, the importance of status differentials between groups, and the measures of their perceived legitimacy and stability. The empirical investigation estimates the infrahumanization levels towards Russians and Belarusians, examines the perceived legitimacy and stability of status differences between Ukrainians and these out-groups, and assesses the strength of Ukrainian group identification. The results reveal a marked degree of infrahumanization towards Russians and Belarusians. Infrahumanization of Russians correlates predictably with Ukrainian identity, perceptions of Russians' antagonistic attitudes (legitimacy) towards Ukrainians, stability of status, and personal experiences of positive communications with Russians. In contrast, the study found no significant correlations when examining infrahumanization of Belarusians.
Architecture and Framework for Group Profiling System in Smart Homes
Authors: Rahul Rahul , Solanki Kamna , Dalal Sandeep , Sudhir Sudhir
Published: September 2023

Smart homes are becoming a progressive reality in our society. Automation and customization are at the center of the functionality of smart homes. User profiles record the user preferences of the inhabitants. User profiles are the heart of smart home systems. Real-world smart homes have multiple residents in them. Most smart homes treat the gathering of users in the same area just as a collection of users, but in real-world scenarios, such a group has its own identity. The proposed system tackles this problem by introducing the notion of Group Profiling. This paper presents the significance of profiles and group profiles in a smart home to achieve better customization and automation.
Probabilistic Principle Component Analysis based Feature Extraction of Embedded System Applications with Deep Neural Network based Implementation in FPGA
Authors: Bachanna Prashant , Gadgay Baswaraj , Chatterjee Sayanti
Published: September 2023

The study of hardware and software systems is of major are very important advent in new devices for communication and progress in system of security. In fast pace mobile and embedded devices application in every day’s life leads some new emerging area for research in data mining field. In this we have some technologies which have demand and error free using the principle of component of PPCA. For Embedded system the applications of PCA is basically applied initially for the lessen the having different qualities especially being to simple of the data. PPCA which have the updated version of PCA which is surveyed by similarity measure. In this work, experiments are extensively carried out, using a FPGA based light weight cryptographic data set having benchmark set to check and illustrate the viability, competence, litheness which are reconfigurable embedded system which are having data mining . Which have FPGA are reconfigurable for the computing architectures for hardware and in neural network. FPGA using the multilayer Cascaded for neural network which are forward in nature (CFFNN) and Deep Neural Network also called as DNN with a huge neuron is still a thought-provoking task. This shortcoming leads to elect the FPGA capacity for a particular application we have used the method of implementation which has two neural network have been implemented and compared , namely, CFFNN and DNN. It can be shown that for reconfigurable embedded system, PPCA based data mining and Machine learning based realization can give more speed up less iteration and more space savings when we have compared it with the static conventional version.
Flip Flops Design in Quantum Dot Cellular Automata Technology: Towards Digitization
Authors: Repe Madhavi , Koli Sanjay
Published: September 2023

Quantum-Dot Cellular Automata (QCA) is a transistor-less technology. In QCA, Columbic repulsion between electrons in the quantum dots makes data transfer possible. This paper presents the design of flip flops using a proposed Rotated-Normal Cells with Displacement (RND) inverter and a cell interaction method. The SR latch, SR Flip Flop (FF), D FF, and T FF are developed using QCA. The proposed D FF gives total and average energy dissipation of 1.31e-002eV and 1.19e-003eV respectively. It also gives a delay of 1 clock phase.  The Proposed T FF provides total and average energy dissipation of 2.40e-002eV and 2.18e-003eV respectively, depicting efficient D FF and T FF in energy dissipation. The proposed SR Flip flop design gives an efficient area. The FFs with the proposed RND inverter and cell interaction method can be the best choice for future Nano communication to construct Nano circuits with less energy dissipation and high speed.
Aspect Based Sentiment Analysis using Various Supervised Classification Techniques: An Overview
Authors: Adagale Sushadevi Shamrao , Gupta Praveen
Published: September 2023

The Sentiment Analysis (SA) work is concerned with identifying aspect terms and categories and categorising emotions (positive, negatively, conflict, and neutral) in ratings and reviews. When it comes to subjectivity, it's typical to divide sentences into objective phrases that include accurate information and subjective statements that include express ideas, beliefs, and perspectives on a given topic. Various existing researchers have already done a lot of work in sentiment analysis with various methods, including aspect extraction. This paper proposed a systematic literature analysis of numerous sentiment analysis using supervised and unsupervised classification techniques. We investigate a few features extraction Natural language Processing (NLP) techniques used to identify aspects of machine learning for the detection of sentiment. An extensive experiment analysis, we discuss the findings of the study, challenges of the current and define the problem statement for the future direction
Integrating Cloud Technology and Artificial Intelligence for Enhanced Real Estate Investment Trusts (REITS) and Crowdfunding Synergy
Authors: Jiang Ting
Published: September 2023

The integration of Cloud technology (CT) and Artificial Intelligence (AI) presents significant potential benefits for the real estate industry, particularly in the areas of REITs and crowdfunding. By providing greater transparency, reducing costs, and increasing liquidity, the cloud can make real estate investments more accessible and appealing to individuals. Moreover, AI can assist with decision-making processes and data analysis, leading to better investment strategies and higher returns. To measure individual perspectives on this integration in India, a survey questionnaire involving 236 individuals will be designed using a Likert scale to measure attitudes and opinions on this topic. This will be followed by data collection through a convenient sampling method and analysis using the SPSS tool. The survey aims to identify the level of awareness and adoption of integrating cloud and AI among real estate professionals and investors, intending to enhance REITs and Crowdfunding Synergy (CS). The findings of the study revealed that the use of CT can enhance transparency in real estate investments, making it easier for investors to understand how their money is being used and what returns they can expect.
Analysis of LTE based an Antenna Design for 5G Communications
Authors: Saida Angotu , Yadav R. K. , Sharma V.K.
Published: September 2023

As the world progresses towards the next generation of communications networks, new technological solutions, architectures and standards are urgently required. This evolution of communications networks will facilitate numerous convincing business and consumer applications and speed up large investments in network infrastructure, appliances and devices. Wireless technologies of the last generation, 5G networks, promise an era of all-round, secure and powerful radio network. The use of 5G technology extends beyond traditional telecoms, and includes extremely low latencies, high energy efficiency or comprehensive Gbps. This paper also accurately includes 5G requirements and antenna categorization accompanied by a comparative study of various antenna designs. The various researchers have previously suggested several 5G antenna designs, but an exhaustive examination using their performance improvement system of the various types of 5G antenna has not yet been completed. We thus tried to examine the various types of 5G antenna design, their mechanisms for improving performance, comparisons and possible worldwide breakthroughs.
A New Cloud with IoT-Enabled Innovation and Skill Requirement of College English Teachers on Blended Teaching Model
Authors: Cao Chang
Published: September 2023

The blended teaching model is a type of educational approach that combines traditional classroom-based instruction with online learning experiences. In this model, students are given access to digital content, resources, and tools, which they can use to supplement their in-person classroom instruction. The blended teaching model is also sometimes referred to as the hybrid learning model. IoT-SDNCT (IoT enabled SDN reinforcement learning with Cloud Technological Innovation and Skill Requirement of College English Teachers on Blended Teaching Model) is a proposed system that aims to revolutionize blended teaching models by leveraging the power of IoT, SDN, and cloud computing technologies. This system incorporates intelligent reinforcement learning algorithms and real-time data analysis to optimize the learning process and improve student engagement and outcomes. In the IoT-SDNCT system, IoT devices such as sensors and wearable technologies are deployed to collect real-time data on student engagement and performance. This data is then transmitted to an SDN controller, which dynamically manages the network infrastructure and optimizes learning pathways. The collected data is also stored and processed in cloud computing platforms, allowing for advanced analytics and personalized feedback for both students and teachers. The key contribution of IoT-SDNCT lies in its ability to adapt the learning process in real-time based on the collected data and intelligent algorithms. This adaptive learning approach enables personalized learning experiences, adjusts the difficulty level of learning tasks, and provides timely feedback to students. Moreover, it empowers teachers with valuable insights and analytics to enhance their teaching strategies and address individual student needs effectively. The proposed system addresses the technological innovation and skill requirements of college English teachers by integrating IoT, SDN, and cloud computing technologies. By utilizing IoT devices, SDN controllers, and cloud platforms, teachers can optimize their teaching methods and create dynamic and interactive learning environments. This not only enhances student engagement but also improves learning outcomes and fosters skill development in both teachers and students. The system's adaptive learning capabilities and real-time data analysis contribute to an enhanced learning experience, increased student engagement, and improved teaching effectiveness.
Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method
Authors: Sai Chennu Naga Venkata , Archana E. , Vivek Bandi , Dhanwanth Batini , K. S. Viknesh
Published: September 2023

Hair fall, a prevalent issue affecting many individuals globally, necessitates early detection for preventive measures and hair health maintenance. Machine learning algorithms have gained attention in predicting hair fall by analysing genetic predisposition, lifestyle habits, and environmental factors. However, the performance of individual algorithms can be improved through ensemble models that combine their strengths. This research paper proposes an ensemble machine learning approach tailored for hair fall prediction. Comparative evaluations with individual algorithms reveal the ensemble models consistently outperform in accuracy, precision, and recall. Leveraging diverse algorithms, the ensemble approach captures a wider range of hair fall patterns, enhancing prediction accuracy. The ensemble models also exhibit higher precision and recall rates, correctly identifying both hair fall and non-hair fall instances. The ensemble models' superiority stems from mitigating the limitations of individual algorithms, resulting in a comprehensive and robust prediction framework. Overall, this research showcases the efficacy of ensemble machine learning models in hair fall prediction, enabling early detection and intervention for hair loss prevention. These findings provide valuable insights for researchers, practitioners, and individuals concerned about hair health.
Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements
Authors: Kulkarni Veena , Khandare Anand
Published: September 2023

Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. Naïve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software.
Deep Learning Framework for Covid-19 Detection and Severity Classification towards Clinical Decision Support System
Authors: Saheb Shaik Khasim , Narayanan B. , Rao Thota Venkat Narayana
Published: September 2023

Chest CT scans are widely used for COVID-19 diagnosis. Existing methods focused more on the detection of the disease. However, there is need for detection of severity towards making decisions for suitable course of action. Towards this end, we proposed a deep learning framework for automatic COVID-19 diagnosis and severity detection. Our framework is based on enhanced Convolutional Neural Network (CNN) model which is found efficient for medical image analysis. We proposed two algorithms to realize the framework. The first algorithm is known as Deep Learning based Automatic COVID-19 Diagnosis (DL-ACD). This algorithm is meant for diagnosis of COVID-19 with learning based phenomena. The second algorithm is known as Automatic COVID-19 Severity Detection (ACSD). It is designed to know severity of the disease which helps in making treatment appropriate. Our framework is evaluated against existing deep learning models and found to have superior performance over the existing models.
The Rise of Crypto Malware: Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats
Authors: Dhotre Dhanraj , Chandre Pankaj R , Khandare Anand , Patil Megharani , Gawande Gopal S
Published: September 2023

Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks.
A Novel Method of Fraud Detection of Credit Cards by Fuzzy, LSTM, and PSO Optimization
Authors: Sree P. Kavya , Kakulapati V. , Indira B.
Published: September 2023

Since online shopping has become so popular, credit card theft has skyrocketed. This makes it hard to spot fake charges on accounts. In this research, credit card fraud detection is performed using a fuzzy database. It has been considered a data mining challenge to reliably identify whether or not a transaction is legitimate. This paper discusses the LSTM method and fuzzy logic. The learning process was sped up and made more precise by using a technique called particle swarm optimization (PSO). Performance values have been compared with those of the LSTM and fuzzy logic techniques, and a PSO-based neural network has been intensively trained by increasing the number of iterations and the population, or number of swarms. It has been shown that the PSO-based algorithm yields the best result for detecting fraudulent transactions. The goal of this method is to lower the mean square error (MSE) rate of the system. PSO is a popular optimization technique that can be used to locate the optimal set of features for the credit card fraud detection system. The proposed method PSO shows less mean squared error compared with Fuzzy and LSTM techniques.
Adaptive Equalization for UWB communication System based on ANFIS
Authors: Miry Mohammed Hussein , Fahad Mohammed Oday
Published: September 2023

Ultra-wideband (UWB) communication systems cover enormous bandwidths that have strongly low-power spectral densities. At UWB communication system with high data rate, owing to multipath propagation, the spread delay in inter symbol interference (ISI) will raise the bit error rate (BER) considerably. ISI which is formed via the UWB channels can be removed by equalization, which is one of the most significant signal processing techniques. Furthermore, LMS algorithm represents a very efficient tool for determining adaptive equalizer coefficients values in communication systems, in spite of that, the LMS adaptive equalizer encounters response diminishing besides slow convergence rate. The current paper adopts an adaptive equalizer based adaptive neuron-fuzzy inference system (ANFIS). The simulation outcomes reveal that the convergence rates as well as accuracy of identification of ANFIS based algorithm are surpass the traditional LMS algorithm, moreover, simulation outcomes prove that ISI is effectively limited and the performance of the system is clearly improved.
A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance
Authors: Bhattacharya Debshree , Nigam Manoj Kumar
Published: September 2023

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for concurrently detecting and classifying several faults using a multilabel classification approach based on binary relevance classifiers. The proposed binary relevance multilabel detection and classification models’ performances are examined. Under both ideal and problematic circumstances, faults in the dataset are collected. A variety of multilabel fault types detection and classification determines the suggested method’s effectiveness.
DQLRFMG: Design of an Augmented Fusion of Deep Q Learning with Logistic Regression and Deep Forests for Multivariate Classification and Grading of Fruits
Authors: Said Archana G. , Joshi Bharti
Published: September 2023

Accurate categorization and grading of fruits are essential in numerous fields, including agriculture, food processing, and distribution. This paper addresses the need for an advanced model capable of classifying and grading fruits more effectively than existing methods. Traditional approaches are limited by their lower precision, accuracy, recall, area under the curve (AUC), and delay. In order to overcome these obstacles, the proposed model combines the capabilities of Deep Q Learning (DQL) for classification and Logistic Regression (LR) with Deep Forests for fruit grading process. Three distinct datasets were used to evaluate the model: the Kaggle - Fruits 360 Dataset, the FRont Experimental System for High throughput plant phenotyping Datasets, and ImageNet samples. In multiple respects, comparative analysis demonstrates that the proposed model outperforms existing methods. Specifically, it achieves a remarkable 4.9% improvement in precision, 5.5% improvement in accuracy, 4.5% improvement in recall, 3.9% improvement in AUC, and an 8.5% reduction in delay levels. Utilizing the strengths of both DQL and LR with Deep Forests, the proposed model achieves its superior performance. DQL, a technique for reinforcement learning, provides the ability to learn and make decisions based on the feedback from the environment. By combining DQL and LR, the classification accuracy is improved, allowing for the precise identification of fruit varieties including Mango, Apple. Papaya, etc. In addition, Deep Forests, a novel framework for ensemble learning, is utilized for fruit grading. Deep Forests utilizes decision trees to effectively capture complex patterns in the data, allowing for dependable and robust fruit grading. Experimental findings indicate that the combination of DQL and LR with Deep Forests yields remarkable performance improvements in fruit classification and grading tasks. Improved precision, accuracy, recall, AUC, and delay indicate the model's superiority over existing methods. This research contributes to the field of fruit classification and grading by developing a sophisticated model that can support a variety of applications in the agriculture, food processing, and distribution industries.
An Effective Dual Level Flow Optimized AlexNet-BiGRU Model for Intrusion Detection in Cloud Computing
Authors: Bingu Rajesh , Jothilakshmi S.
Published: September 2023

In recent years, several existing techniques have been developed to solve security issues in cloud systems. The proposed study intends to develop an effective deep-learning mechanism for detecting network intrusions. The proposed study involves three stages pre-processing, feature selection and classification. Initially, the available noises in the input data are eliminated by pre-processing via data cleaning, discretization and normalization. The large feature dimensionality of pre-processed data is reduced by selecting optimal features using the wild horse optimization-based feature selection (WHO-FS) model. The selected features are then input into a proposed dual-level flow optimized AlexNet-BiGRU detection model (DLFAB-IDS). Whereas the flow direction algorithm (FDA) approach optimally tunes the hyperparameters and helps to enhance the classification performance. In the proposed model, the intrusions are detected by AlexNet and the multiclass classification is performed through the BiGRU method. The proposed study used the NSL-KDD dataset, and the simulation was done by Python tool. The efficacy of a proposed model is measured by evaluating several performance metrics. The comparison over other existing techniques shows that the proposed model brings higher performance in terms of accuracy 96.81%, recall 95.84%, precision 96.24%, f1-score 96.75%, prediction time 0.43s and training time 152.84s.
Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET
Authors: Vinay Kumar K. , Venkatramulu S. , Rao V. Chandra Shekar , Srinivas C. , Pratapagiri Sreenivas , Raghuram B.
Published: September 2023

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach.
Regression and Classification of Alzheimer’s Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image
Authors: Reddy K. Shirisha , Arjun N. , Mudiraj Kowkuri Hrushikesh
Published: September 2023

Because of headways in deep learning and clinical imaging innovation, a few specialists are presently utilizing convolutional neural networks (CNNs) to extricate profound level properties from clinical pictures to all the more exactly classify Alzheimer's disease (AD) and expect clinical scores. A limited scale profound learning network called PCANet utilizes principal component analysis (PCA) to make multi-facet channel banks for the incorporated learning of information. Blockwise histograms are made after binarization to get picture ascribes. PCANet is less versatile than different frameworks since the multi-facet channel banks are made involving test information and the produced highlights have aspects during the many thousands or even many thousands. To conquer these issues, we present in this study a PCANet-based, information free organization called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). To deliver the last picture highlights, we first form higher-request tensors and utilize tensor decomposition (TD) to achieve information dimensionality decrease. Specifically, we foster staggered channel banks for test getting the hang of utilizing nonnegative matrix factorization(NMF) as opposed to PCA. These properties serve as input to the support vector machine (SVM) that our technique employs to diagnose AD, forecast clinical score, and categorise AD.
Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System
Authors: Mane Deepak , Upadhye Gopal , Gite Vinit , Sarwade Girish , Kamble Gourav , Pawar Aditya
Published: September 2023

SVM-based Real-time Pose Detection and Correction System refer to a computer system that uses machine learning techniques to detect and correct a person's yoga pose in real-time. This system can act as a virtual yoga assistant, helping people improve their yoga practice by providing immediate feedback on their form and helping to prevent injury. This paper presents a yoga tracker and correction system that uses computer vision and machine learning algorithms to track and correct yoga poses. The system comprises a camera and a computer vision module that captures images of the yoga practitioner and identifies the poses being performed. The machine learning module analyzes the images to provide feedback on the quality of the poses and recommends corrections to improve form and prevent injuries. This paper proposed a customized support vector machine (SVM) based real-time pose detection and correction system that suggests yoga practices based on specific health conditions or diseases. Paper aims to provide a reliable and accessible resource for individuals seeking to use yoga as a complementary approach to managing their health conditions. The system also includes a practitioner’s interface that enables practitioners to receive personalized recommendations for their yoga practice. The system is developed using Python and several open-source libraries, and was tested on a dataset of yoga poses. The hyper parameter gamma tuned to optimize the classification accuracy on our dataset produced 87% which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting yoga poses, and its potential to enhance the quality of yoga practice.
Designing of Real-time Communication Method to Monitor Water Quality using WSN Based on IOT
Authors: Singh Joginder , Srivastava Alok , Dalal Vipin
Published: September 2023

Data accuracy has always been the essential and foremost requirement of any communication. A Real-time Water quality-monitoring system (WQMS) needs high-level data accuracy to process the water perfectly for the desired usage in any specific purpose.  Exponential growth in man-made processes, human activities, Industrialization, and economic growth along with depleting safe water resources has made the water pollution issue a foremost threat to human survival and human civilisation as a whole. The Conventional WQMS lacks in providing data accuracy while testing and analysing the water samples at sites due to improper data transmission, human intervention, instrument node working and calibration issues. Therefore, monitoring the quality of the water is essential with a prime focus on data accuracy through proper testing, data analysis and data transmission methods to provide real-time data accuracy. In this research work, an IOT-based wireless sensor network (WSN) is proposed that uses mesh networking to connect the sensor nodes and message queuing telemetry transfer (MQTT) protocol to send the acquired data to a cloud server ADDA Fruit IO. ESP32 standalone microcontroller with in-built Wi-Fi is used as a transceiver on sensor nodes and master nodes. As far as sensor and master node power is concerned, a self-adapting power generation system is incorporated using solar power and water energy harvesting techniques. The sensor nodes are calibrated as per WHO standards using deionized water and buffer capsules. Five random samples are collected from river water, pond water, Borewell water, R.O water and Municipal committee water to analyse the proposed system’s accuracy. The accuracy test and analysis is done using statistical tools on water sample measurements by the proposed sensor node, and the same is compared with the actual certified instrument, hardware manual-based measurement, Laboratory value check and transmitted value on Cloud Server. The proposed WQMS is designed to measure various WQM parameters i.e. Total Dissolve Solid (TDS), pH, Temperature and turbidity and to ensure data accuracy.
Enhanced Study of Deep Learning Algorithms for Web Vulnerability Scanner
Authors: Bamane Kalyan Devappa , Patankar Abhijit Janardan , Gupta Priyanka , Jambi Ratnaraja Kumar , Rajgure Nitisha
Published: September 2023

The detection of online vulnerabilities is the most important task for network security. In this paper, deep learning methodologies for dealing with tough or complicated challenges are investigated using convolutional neural networks, long-short-term memory, and generative adversarial networks.Experimental results demonstrate that deep learning approaches can significantly outperform standard methods when compared to them. In addition, we examine the various aspects that affect performance. This work can provide researchers with useful direction when designing network architecture and parameters for identifying web attacks.
Design and Performance Analysis of a Rectangular Microstrip Patch Antenna for Wireless Communication at 2.4 GHz
Authors: Verma Shreyashree , Kumar Anil , Agrawal Neelesh
Published: September 2023

This paper presents the design of a rectangular Microstrip Patch Antenna (MPA) intended for operation at 2.4 GHz frequency, predominantly used in wireless communication. The proposed MPA, designed on an FR4-Epoxy substrate using a microstrip line feeding approach, was simulated using the High-Frequency Structure Simulator (HFSS). The primary metrics evaluated included return loss, gain, Voltage Standing Wave Ratio (VSWR), and half-power beamwidth. The findings revealed an impressive return loss of -25dB and a moderate gain of 1.48 dB at the target frequency. The VSWR value was approximately 1, indicating efficient power transmission with minimal reflections. Furthermore, the antenna exhibited a broad half-power beamwidth of 79 degrees, suggesting its potential for applications requiring extensive signal coverage. This research provides insights into MPA design principles and serves as a foundation for future advancements in wireless communication antenna systems.
Expressivity of Tweets on Social Issues Using Aspect Based Text Classification
Authors: Thomas Elizabeth Leah , Parthasarathy Subashini
Published: September 2023

Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes and Gender Equality and Violence Against Women.

The study employs aspect-based sentiment analysis to classify sentiment polarity in tweets, utilising a comprehensive framework involving three text feature classification stages. The initial stage analyses individual words, phrases, and tweet patterns to classify text features based on specific linguistic elements. In the subsequent step, semantic relations explore a better understanding of the core sentiment and infer relationships between different text keywords. This stage enhances the analysis by considering the meaning and contextual nuances of the language used in the tweets. The final stage incorporates transformer-based models for effective multilabel classification to view the diversity present in the dataset. The study's quantitative analysis reveals that the Ensemble learning approach demonstrates an impressive precision measure of 93%. By integrating the three stages of text feature classification, the study enhances the accuracy and comprehensiveness of sentiment analysis in social discussions about crime on Twitter.
Enhancing Auction Systems with Blockchain Technology
Authors: Shelke Priya , Mirajkar Riddhi , Dedgaonkar Suruchi , Bhimanpallewar Ratnmala , Shewale Chaitali , Kadam Sandeep
Published: September 2023

This research paper examines the use of blockchain technology in auction systems. Traditional auction systems face issues related to trust, transparency, and security. Blockchain offers a decentralized and immutable solution that can enhance the efficiency, security, and transparency of auctions. The paper provides an overview of blockchain technology and identifies the challenges in traditional auctions that blockchain can address. It explores existing blockchain-based auction systems and evaluates their effectiveness in mitigating issues such as bid manipulation and fraud. The impact of blockchain on auction participants is also discussed, including benefits like increased trust and reduced transaction costs, as well as challenges related to adoption and scalability. The paper considers both theoretical and practical aspects, analyzing case studies and implementation challenges. It concludes by summarizing the key findings and suggesting future research directions to advance the application of blockchain in auction systems. The auction contract allows users to place bids and determine the highest bidder within a specified time period. The contract also provides functionality for canceling the auction and finalizing it by transferring the funds to the appropriate recipients.
AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity
Authors: Gandhi K Indra
Published: September 2023

The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality.
Independent Weighted Feature Set with Linked Feature Reduction Model for Lung Cancer Stage Detection using Machine Learning Model
Authors: Kumar Makineni.Siddardha , Rao Kasukurthi Venkata
Published: September 2023

Lung cancer is a potentially fatal disease that is affected to 18% of population every year. Finding the exact location of a cancer and identification of lung cancer stage continues to be difficult for medical professionals. The true reason for cancer and a comprehensive cure is still unknown. Treatment for cancer is possible if detected at an early stage with accurate stage detection. Finding areas of the lung that have been impacted by cancer requires the use of image processing techniques like noise reduction, highlight filtration, recognizable proof of effected lung regions, and perhaps a comparison with data on the curative history of lung cancer. This research investigates whether or not technology enabled by machine learning algorithms and image processing can correctly classifies and predict lung cancer. For images, the dimensional feature channel is used in the preliminary processing stage. The proposed model considers Magnetic Resonance Imaging (MRI) images for detection of lung cancer. This research proposes an Independent Weighted Feature Set with Linked Feature Reduction (IWFS-LFR) model for accurate lung cancer stage detection based on the size of the tumour. The tumour stage can be accurately predicted using the feature attribute similarity calculation for accurate detection of lung cancer stage for proper diagnosis. The proposed model when contrasted with the traditional model exhibits better performance.