bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Activity Area Localization in Functional MRI Imaging with Deep Learning based Automatic Segmented Brain Tumor for Presurgical Tumor Resection Planning
Authors: Ingle Archana , Roja Mani , Sankhe Manoj , Patkar Deepak
Published: September 2023

Functional Magnetic Resonance Imaging (fMRI) determines small blood flow variations that arise due to brain activity. fMRI major study is about functional anatomy which determines the area of the brain controlling vital functions such as hand and foot motor movements for both left and right, speech mantra, and speech word activities. For this instinctive localization of activity areas for specific tasks is very important. This paper appropriately describes the fMRI paradigm timeline with a modified fMRI paradigm timeline due to the hemodynamic response function (HRF).   Efficient activity area localization of thirty-three patients for fMRI data acquired from the hospital is achieved with dynamic thresholding. Dynamic thresholding is also effective in removing excess highlighted areas which helps in the reduction in expert efforts and time required to generate the patient report.  The localize activity area is further mapped with deep learning-based automatic segmented brain tumor regions to find overlapping regions. The exact location of the overlapping region is recovered which helps with preoperative counseling and tumor resection planning. All the results are verified and validated by two expert radiologists from the Hospital.
ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade
Authors: Indira D.N.V.S.L.S. , Tenali Nagamani , Kumar J N V R Swarup , Karthik Jayavarapu , Lakshmi M. Venkata , Jelciya Ch. , Srinivas G. Sai
Published: September 2023

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making  it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.

The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.
A Behaviour Study on Cloud Eco-System: Data Security Perspective
Authors: Gaur Niyati , Singh Shashank
Published: September 2023

Cloud eco-system is a revolution now, which modifies the way in which the IT-based services are being delivered to end customers. It is increasing or we can also say grown-up technology that delivers multiple benefits whether in terms of economics or in terms of cost-effective resource utilization. The ability to install and improve their services on that platform is made possible by the advent of cloud computing, which opens up new options for long-term solutions. Cloud computing's environmental and economic impacts must be considered while assessing its long-term viability. A growing number of organizations, businesses, and personal users are depending on services supplied by the cloud and keeping crucial information in the cloud because of its easy-to-use characteristics. The cloud, despite its widespread use, nevertheless has a number of drawbacks when it comes to data security. Customers are concerned about how their personal information is transported to and from the cloud. Research articles in this topic have been thoroughly analyzed and examined in this report.
Analysis of Behavioral Characteristics of Multiple Blackhole Attacks with TCP and UDP Connections in Mobile ADHOC Networks based on Machine Learning Algorithms
Authors: Nandini Kagita , Thammana Sasidhar , Pitta Vivek , Prathyusha Segu , Ram Varikuti Jaswanth
Published: September 2023

In Mobile Adhoc Networks (MANET’s), a suit of nodes which are under mobility work together to transmit data packets in a multiple-hop manner without relying on any fixed or centralized infrastructure. A significant obstacle in managing these networks is identifying malicious nodes, or "black holes". To detect black holes, we proposed a method involves broadcasting a Cseq to the neighboring nodes and awaiting the node's response is utilized. This Network is simulated with 25 number of nodes connected with TCP connection and observed the different behavioural characteristics of nodes. Then the connections are changed to UDP and observed the characteristics. Then characteristics are analyzed with different machine learning algorithms. The network is simulated in NS2 environment.
Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey
Authors: Musyoka Faith Mueni
Published: September 2023

The Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues.
An Efficient Probabilistic Deep Learning Model for the Oral Proficiency Assessment of Student Speech Recognition and Classification
Authors: Li Wenyi , Mohamad Maslawati
Published: September 2023

Natural Language Processing is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. Speech recognition systems utilize machine learning algorithms and statistical models to analyze acoustic features of speech, such as pitch, duration, and frequency, to convert spoken words into written text. The Student English Oral Proficiency Assessment and Feedback System provides students with a comprehensive evaluation of their spoken English skills and offers tailored feedback to help them improve. It can be used in language learning institutions, universities, or online platforms to support language education and enhance oral communication abilities. In this paper constructed a framework stated as Latent Dirichlet Integrated Deep Learning (LDiDL) for the assessment of student English proficiency assessment. The system begins by collecting a comprehensive dataset of spoken English samples, encompassing various proficiency levels. Relevant features are extracted from the samples, including acoustic characteristics and linguistic attributes. Leveraging Latent Dirichlet Allocation (LDA), the system uncovers latent topics within the data, enabling a deeper understanding of the underlying themes present in the spoken English. To further enhance the analysis, a deep learning model is developed, integrating the LDA topics with the extracted features. This model is trained using appropriate techniques and evaluated using performance metrics. Utilizing the predictions made by the model, the system generates personalized feedback for each student, focusing on areas of improvement such as vocabulary, grammar, fluency, and pronunciation. Simulation mode uses the native English speech audio for the LDiDL training and classification. The experimental analysis stated that the proposed LDiDL model achieves an accuracy of 99% for the assessment of English Proficiency.
Two-Factor Biometric Identity Verification System for the Human-Machine System Integrated Deep Learning Model
Authors: Zhu Chaoyang
Published: September 2023

The Human-Machine Identity Verification System based on Deep Learning offers a robust and automated approach to identity verification, leveraging the power of deep learning algorithms to enhance accuracy and security. This paper focused on the biometric-based authentical scheme with Biometric Recognition for the Huma-Machinary Identification System. The proposed model is stated as the Two-Factor Biometric Authentication Deep Learning (TBAuthDL). The proposed TBAuthDL model uses the iris and fingerprint biometric data for authentication. TBAuthDL uses the Weighted Hashing Cryptographic (WHC) model for the data security. The TBAuthDL model computes the hashing factors and biometric details of the person with WHC and updates to the TBAuthDL. Upon the verification of the details of the assessment is verified in the Human-Machinary identity. The simulation analysis of TBAuthDL model achieves a higher accuracy of 99% with a minimal error rate of 1% which is significantly higher than the existing techniques. The performance also minimizes the computation and processing time with reduced complexity.
A New Cloud Computing-Based Assessment of Issues in Online Teaching Management in the Post-Epidemic Era of COVID-19
Authors: Zhao Zichun
Published: September 2023

The COVID-19 pandemic has accelerated the development of online teaching and learning, and educational institutions are now facing new challenges and opportunities. Online teaching management has become a crucial issue in ensuring the quality of education in the post-epidemic era. Online teaching management is a complex issue that requires schools to provide technical support, develop interactive teaching methods, ensure fair assessment, provide time management support, and offer training and support to teachers. By addressing these challenges, schools can ensure the effectiveness and quality of online teaching in the post-epidemic era. This paper examined the issues and challenges associated with Online Teaching Management in the Post-Epidemic era of CoVID -19. The analysis is conducted in a study of China. The GAN_OTM (Generative Adversarial Network for Online Teaching Management) model is proposed as a solution to address the challenges faced in online teaching management during the post-epidemic era of COVID-19. This model leverages the power of deep learning and generative adversarial networks to generate realistic virtual classrooms and learning environments, personalized learning experiences, and provide real-time feedback to students and teachers. With developed GAN_OTM model discussed its potential benefits and contributions to online teaching management, including improved student engagement, personalized learning experiences, and scalability. Furthermore, highlight the performance metrics used to evaluate the GAN_OTM model and the importance of analyzing its results. In the other hand, teachers are not comfortable with online teaching management to interact with students.
Interactive IoT Cloud Deep Learning Model for Research Development in Universities for the Educational Think Tank
Authors: Jiang Yu , Liang Mingwei
Published: September 2023

The construction of university education think tanks using the interactive service platform enables the sharing of research resources, encourages cross-disciplinary research collaboration, and fosters innovation in education. It also helps to build a stronger relationship between academia and industry by enabling practitioners to participate in research activities. The Internet of Things (IoT) can be used to collect and analyze data from various sources, including sensors and other connected devices, to provide insights into education-related issues. The integration of these technologies in university education thinks tanks can help to enhance the efficiency and effectiveness of research, decision-making, and collaboration processes. Hence, this paper constructed an Interactive IoT Cloud Computing Platform (IIoTCC). With IIoTCC model the innovative idea about research and other ideas are collected and stored in a Cloud environment. Within the environment, information collected is stored in the stacked architecture model with the voting-based model. Through the stacked model, information is processed and evaluated for academic activities. The IoT environment is implemented through IIoTCC for the information process in a deep learning environment for academic-related issues. Simulation analysis expressed that IIoTCC model achieves a higher accuracy rate of 99.34% which is significantly higher than conventional classifiers.
An Efficient Big Data Visualization Deep Learning Architecture Model for Path Selection of College Students through Moral Education
Authors: Du Yujie , Xie Zongshun
Published: September 2023

Visualization technology can be used to present the analysis results in a more intuitive and easy-to-understand way, which can help educators to better understand the moral education needs of college students, and adjust their teaching strategies accordingly. The combination of big data analysis and visualization technology can also help to improve the efficiency and effectiveness of moral education in colleges and universities. The research on the moral education path selection of college students based on big data visualization has great significance for promoting the development of moral education in colleges and universities, and for cultivating high-quality talent with good moral character. This paper proposed an Optimization model for big data analytics for moral education. The data associated with moral education and information are stored in cloud with the big data. The stored big data visualization process is performed with the optimization model for the feature extraction. The optimization is performed with an integrated Flamingo and weighted black widow Optimization model. The proposed model is stated as the Integrated Flamingo Black Widow (IFBW) model. The performance of the IFBW model is implemented with the deep learning Restricted Boltzmann Machine (RBM) architecture. Simulation analysis stated that IFBW model achieves a higher classification accuracy rate of 99% with a minimal error rate.
Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol
Authors: Rao V. Chandra Shekhar , VinayKumar K. , Pratapagiri Sreenivas , Srinivas C. , Raghuram B. , Venkatramulu S.
Published: September 2023

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads.
Detection of Malware in Large Networks using Deep Auto Encoders
Authors: Janani K. , Gunasundari R.
Published: September 2023

Data mining and machine learning have been heavily studied in recent years with the purpose of detecting sophisticated malware. The majority of these approaches rely on architectures that do not involve deeply enough into the learning process, despite the fact that they have yielded excellent results. This is because deep learning is finding increasing application in both business and academia thanks due to its skills in feature learning. In this paper, we develop a Deep Auto Encoder (DAE) based detection mechanism to detect the malwares crawling in the large scale networks. The DAE act as an unsupervised deep learning model that helps in detecting the malwares. The simulation is conducted on two different datasets to test the robustness of the model. The results show that the proposed method has higher rate of accuracy in detecting the attacks than other methods.
Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python
Authors: Sinha Arvind Kumar , Akhtar Md. Amir Khusru , Kumar Mohit
Published: September 2023

The objective of AI-based resume screening is to automate the screening process, and text, keyword, and named entity recognition extraction are critical. This paper discusses segmenting resumes in order to extract data and perform text analysis. The raw CV file has been imported, and the resume data cleaned to remove extra spaces, punctuation and stop words. To extract names from resumes, regular expressions are used. We have also used the spaCy library which is considered the most accurate natural language processing library. It includes already-trained models for entity recognition, parsing, and tagging. The experimental method is used with resume data sourced from Kaggle, and external Source (MTIS).
Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks
Authors: Venkatakrishnamoorthy T. , Srinivas Bachu , Dharani M. , Krishna S. Vamsee , Reddy Y. Dasaratha Rami , Supriya P.
Published: September 2023

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput.
Improving Deep Learning for Seizure Detection using GAN with Cramer Distance and a Temporal-Spatial-Frequency Loss Function
Authors: Palanichamy Indurani , Sundaram Veni
Published: September 2023

The signals of EEG are analyzed in the identification of seizure and diagnosis of epilepsy. The visual examination process of EEG data by skilled physician is huge time-utilization and the judgemental process is complicated, which may vary or show inconsistency among the physician. Hence, an automatic process in diagnosis and detection was initiated by the Deep Learning (DL) approaches. Time Aware Convolutional Neural Network with Recurrent Neural Network (TA-CNN-RNN) was one among them. Deep neural networks trained on large labels performed well on many supervised learning tasks. Creating such massive databases takes time, resources, and effort. In many circumstances, such resources are unavailable, restricting DL adoption and use. In this manuscript, Generative Adversarial Networks with the Cramer distance (CGAN) is proposed to generate an accurate data for each lable. A spatiotemporal error factor is introduced to differentiate actual and genetrated data. The discriminator is learned to differentiate the created data from the actual ones, while the generator is learned to create counterfeit data, which are not estimated as false by the discriminator. The classical GANs have a complex learning because of the nonlinear and non-stationary features of EEG data which is solved by Carmer Distance in the proposed method. Finally, the sample generated by CGAN is given as input for the Time Aware Convolutional Neural Network with Recurrent Neural Network (TA-CNN-RNN) classifier to investigate experimental seizure Prediction outcome of the proposed CGAN. From the investigational outcomes, the proposed CGAN- TA-CNN-RNN model attained classification accuracy of 94.6%, 94.8% and 95.2% on CHB-MIT-EEG, Bonn-iEEG and VIRGO-EEG than other existing EEG classification schemes and also provides great potentials in real-time applications.
A Complete Design of Smart Wind Farm Enriched with Novel Anemometer
Authors: Revathi K. , Tamilselvi T. , Theresa W. Gracy , Dhivya M.
Published: September 2023

Renewable energy are endless and environment friendly sources of power production and also it is considered as the alternative of non- sustainable energy sources like coal, fossil fuels and nuclear power. Wind Energy is accounted as one of the fast depleting, pollution free energy source compared to hydro power and thermal power. Internet of Things (IoT), which is a wireless, self configuring network of sensors powered with communication facility promotes the facility of remote monitoring and control activities in smarter way without human intervention. Likewise machine learning is another technological giant offers accurate prediction over the voluminous data and imposes intelligence to the machines kept for operation. The primary objective is to develop an IoT based wind farm module that enables installed capacity identification, structural monitoring and scope for power generation in that locality.
Malicious URL Website Detection using Selective Hyper Feature Link Stability based on Soft-Max Deep Featured Convolution Neural Network
Authors: Pushpalatha M. , Vijaya A.
Published: September 2023

The web resource contains many domains with different users' Uniform Resource Locators (URLs). Due to the increasing amount of information on the Internet resource, malicious activities are done by hackers by expecting malicious websites in URL sub-links. Increasing information theft leads data sources to be vested in huge mediums. So, to analyze the web features to find the malicious webpage based on the deep learning approach, we propose a Selective Hyper Feature Link stability rate (SHFLSR) based on Soft-max Deep featured convolution neural network (SmDFCNN) for identifying the malicious website detection depends on the actions performed and its feature responses. Initially, the URL Signature Frame rate (USFR) is estimated to verify the domain-specific hosting. Then the link stability was confirmed by post-response rate using HyperLink stability post-response state (LSPRS). Depending upon the Spectral successive Domain propagation rate (S2DPR), the features were selected and trained with a deep neural classifier with a logically defined Softmax- Logical activator (SmLA) using Deep featured Convolution neural network (DFCNN). The proposed system performs a high-performance rate by detecting the malicious URL based on the behavioral response of the domain. It increases the detection rate, prediction rate, and classifier performance.
Performance Analysis of Microservices Behavior in Cloud vs Containerized Domain based on CPU Utilization
Authors: Jhingran Sushant , Rakesh Nitin
Published: September 2023

Enterprise application development is rapidly moving towards a microservices-based approach. Microservices development makes application deployment more reliable and responsive based on their architecture and the way of deployment. Still, the performance of microservices is different in all environments based on resources provided by the respective cloud and services provided in the backend such as auto-scaling, load balancer, and multiple monitoring parameters. So, it is strenuous to identify Scaling and monitoring of microservice-based applications are quick as compared to monolithic applications [1]. In this paper, we deployed microservice applications in cloud and containerized environments to analyze their CPU utilization over multiple network input requests. Monolithic applications are tightly coupled while microservices applications are loosely coupled which help the API gateway to easily interact with each service module. With reference to monitoring parameters, CPU utilization is 23 percent in cloud environment. Additionally, we deployed the equivalent microservice in a containerized environment with extended resources to minimize CPU utilization to 17 percent. Furthermore, we have shown the performance of the application with “Network IN” and “Network Out” requests.
Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model
Authors: Zhu Chaoyang
Published: September 2023

Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with  Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction.
Smart ATM Security and Alert System with Real-Time Monitoring
Authors: Thopate Kaushalya , Musale Prajakta , Dandavate Prajkta , Jadhav Balasaheb , Cholke Puja , Bhatlawande Shripad , Shlaskar Swati
Published: September 2023

The growing adoption of automated teller machines (ATMs) worldwide, ensuring the security of ATM transactions and protecting customer data has become a critical concern for the banking industry. In this research paper, we proposed an Internet of Things (IoT) based ATM security system using the NodeMCU ESP8266 module, PIR (Passive Infrared) sensor, LCD display with I2C interface, and the Telegram app. The proposed system utilizes the NodeMCU ESP8266 module, which is a low-cost Wi-Fi enabled microcontroller, as the main controller for the security system. The PIR sensor is used to detect motion or presence near the ATM, which can potentially indicate unauthorized access. The LCD display with I2C interface is used to provide real-time status information about the ATM, such as Motion Detected or NO Motion. When the PIR sensor detects any suspicious activity near the ATM, the NodeMCU ESP8266 module sends a notification to the ATM owner or security personnel via the Telegram app. The notification includes details about the detected activity, allowing the owner or security personnel to take immediate action. The real- time alerts enable prompt response to potential security breaches and help prevent unauthorized access to the ATM.
Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records
Authors: Aelgani Vivekanand , Vadlakonda Dhanalaxmi
Published: September 2023

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction.
MANET Congestion Control Mechanism - Challenges and Survey
Authors: Hemalatha S. , Patel Nileshkumar , Jain Neelesh Kumar , Kumar Ajay , Kaulage Anant Nagesh , Mishra Amit Kumar
Published: September 2023

The transport layer plays a crucial role in the Mobile Adhoc Network (MANET) protocol stack by controlling traffic flow, managing congestion, and enabling end-to-end delivery. With the help of congestion control mechanisms, numerous protocols are formed to enhance MANET performance. This paper focuses on a thorough analysis of the challenges the MANET protocol stack is facing as a result of congestion control issues such high overload, long delays, and increased packet loss. Finally, note that in order to increase MANET performance, research needs to concentrate on specific congestion control mechanisms.
Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic
Authors: Swathi K. , Narsimha G.
Published: September 2023

The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.
Multiclass Classification of Brain MRI through DWT and GLCM Feature Extraction with Various Machine Learning Algorithms
Authors: Suryawanshi Shweta , Patil Sanjay B.
Published: September 2023

This study delves into the domain of medical diagnostics, focusing on the crucial task of accurately classifying brain tumors to facilitate informed clinical decisions and optimize patient outcomes. Employing a diverse ensemble of machine learning algorithms, the paper addresses the challenge of multiclass brain tumor classification. The investigation centers around the utilization of two distinct datasets: the Brats dataset, encompassing cases of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG), and the Sartaj dataset, comprising instances of Glioma, Meningioma, and No Tumor. Through the strategic deployment of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) features, coupled with the implementation of Support Vector Machines (SVM), k-nearest Neighbors (KNN), Decision Trees (DT), Random Forest, and Gradient Boosting algorithms, the research endeavors to comprehensively explore avenues for achieving precise tumor classification. Preceding the classification process, the datasets undergo pre-processing and the extraction of salient features through DWT-derived frequency-domain characteristics and texture insights harnessed from GLCM. Subsequently, a detailed exposition of the selected algorithms is provided and elucidates the pertinent hyperparameters. The study's outcomes unveil noteworthy performance disparities across diverse algorithms and datasets. SVM and Random Forest algorithms exhibit commendable accuracy rates on the Brats dataset, while the Gradient Boosting algorithm demonstrates superior performance on the Sartaj dataset. The evaluation process encompasses precision, recall, and F1-score metrics, thereby providing a comprehensive assessment of the classification prowess of the employed algorithms.
Novel Frame Work for Blockchain Based Votingapplication Using Ethereum Virtual Machine
Authors: Srilakshmi P , Shareef D K , Jayudu T Venkata Naga , Chaithanya D. , Sonavane Sonali Mahendra , Anushna S.D.
Published: September 2023

Blockchain fills in as a scattered record development which licenses propelled assets for be executed in decentralized framework which works in a common manner. In bound together systems everything depends upon the central structure and the outcomes of throwing a voting form system were not exact considering the way that anyone can change. These kinds of results are wrong and not trustworthy by the people who are voting. The Voting machines that are accessible as of now rely upon the servers which are centralized. Here the people who are voting have to keep belief on the concentrated individuals for the accurate results. So, the contemplating decentralized law-based systems that can settle on the political choice procedure very snappy and easy. These scattered systems are the most praised advancement improvement in this present genuine world. Here Blockchain advancement has various wide extent of usages starting and beginning from distributing statistics, economics etc. Here the blockchain advancement is used as a help of achieve this just assembled application that depends concerning a decentralized appropriated application. Here, the structure works in the way as follows, the transactions or understandings whichever executed are changed over into machine reasonable method of reasoning that engages and ensures understanding among various people who are in the organization, and in like manner who has the solidarity to support their individual money exchanges, different activities and opposite party challenges that are associated with their checking and observing. Smart understandings will be enabled and they are to be passed on into a blockchain space or stage, the amount of possible use cases for this development will be checked and improves amazingly. The most critical use of this blockchain is that to empty the prerequisite for pariahs in the two individuals when all is said in done and the private parts, to end up being progressively capable and effective.
Study on Software Defect Prediction Based on SVM and Decision Tree Algorithm
Authors: Rao Gauri , Pujari Disha Gundo , Khalkar Rohini G. , Medhe Vidya Atish
Published: September 2023

Software Defect Prediction is a process of identifying the potential defects in software systems before they occur. In this approach, the dataset containing information about software attributes is used as input, and the output is the prediction of whether the software is defective or not. The input dataset is generally in the form of a CSV file, which contains various software attributes such as cyclomatic complexity, essential complexity, Design Complexity, etc. The output of the defect prediction is binary classification. It is done by using SVM (Support Vector Machine) and a decision tree algorithm. This approach can help software developers identify their systems' defects before they cause any harm or affect the system’s performance.
Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach
Authors: Patil Dipali Himmatrao , Gadekar Amit
Published: September 2023

Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts.
Secure Routing Protocols Comparison Analysis Between RNBR, SAA, A-UPK
Authors: Reddy T. Shekar , Prasad T. Shyam , Rama Devi Y.
Published: September 2023

The advent of wireless communications and the development of mobile devices have made great strides in the development of roaming communications. The MANET mobile network was developed with the ability for mobile devices to quickly self-configure and extend wireless coverage without infrastructure support. Security is one of the most important areas of research and plays a vital role in determining the success of personal and commercial telephone systems.Therefore, this study focuses on systematically examining MANET security and accountability issues and analyzing the performance of solutions proposed by three different design approaches to security systems.First, it provides an approach for identifying trusted nodes employing the proposed RNBR method for secure routing.it provides a Self-Assured Assessment (SAA) method to estimate node stability. Its main goal is to contribute to a self-assessment-based reliability assessment mechanism that provides a reliable and reliable pathway.it provides a new authentication method to prevent forgery attacks. It supports authentication mechanisms to prevent RF attacks and ensure secure routing development.The main Objective of this paper is compare to packet delivery Ratio ,Control Overhead, Packet Drop Ratio in different secure RNBR,SAA,A-UPK Routing Protocols in MANETS.
Reinforcement of the Bank Loan Model using the Feature Selection Method of Machine Learning
Authors: Goel Noopur , Singh Durgesh Kumar
Published: September 2023

Does feature selection and machine learning (ML) guarantee the effectiveness of the bank credit system model? This article aims to analyze this problem. In fact, in finance, expert-based credit risk models still dominate. In this study, we establish a new benchmark using consumer data and present machine learning methods. A risk prediction that is as accurate as possible is an important requirements for credit scoring models. In addition, regulators expect that the models should to be auditable and transparent. As a result, the superior predictive power of contemporary machine learning algorithms cannot be fully utilized in credit scoring because very simple predictive models, such as several ML classifiers, are still widely used. As a result, significant potential is missed, increasing reserves or the number of credit defaults. A framework for comparing scores before and after feature selection machine learning models that are transparent, auditable, and explainable is presented in this article, as well as the various dimensions that need to be taken into consideration in order to make credit scoring models understandable. In accordance with this framework, we give an overview of the models which demonstrate how it can be used in credit scoring, and compare the results to scorecards' interpretability. The model presented demonstrates that machine learning techniques can maintain their ability to enhance predictive power while still maintaining a comparable level of interpretability.
A Parameter Based Comparative Study of Deep Learning Algorithms for Stock Price Prediction
Authors: Paygude Priyanka , Singh Anchal , Tripathi Esha , Priya Shruti , Gayakwad Milind , Chavan Prashant , Chaudhary Snehal , Joshi Rahul , Kotecha Ketan
Published: September 2023

Stock exchanges are places where buyers and sellers meet to trade shares in public companies. Stock exchanges encourage investment. Companies can grow, expand, and generate jobs in the economy by raising cash. These investments play a crucial role in promoting trade, economic expansion, and prosperity. We compare the three well-known deep learning algorithms, LSTM, GRU, and CNN, in this work. Our goal is to provide a thorough study of each algorithm and identify the best strategy when taking into account elements like accuracy, memory utilization, interpretability, and more. To do this, we recommend the usage of hybrid models, which combine the advantages of the various methods while also evaluating the performance of each approach separately. Aim of research is to investigate model with the highest accuracy and the best outcomes with respect to stock price prediction.
Perception based User Profiles for Web Personalization
Authors: M P Sowbhagya , H K Yogish , Raju G T
Published: September 2023

Personalized web services reduce the burden of information overload by collecting facts that match the needs of the user. An important aspect of personalized web services is the creation of user profiles that contain user information and settings. This article introduces a unique method called Perception-Based User Profiles (PUP) based on perception and browsing order, develops and updates user profiles. User profiles include perceptions and relationships, which can help guarantee that user interests are represented semantically. Second, when calculating the perception and duration of the relationship, for each site in a session, the user's browsing order is considered. Third, cognitive psychometric memory model is used to update the user profile's perceptions and relationships at the end of each session, ensuring the user profile's dynamics. The results of the tests suggest that this strategy works well for building and updating user profiles.
Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction
Authors: Anisha Anisha , Sabharwal Munish , Tripathi Rohit
Published: September 2023

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately.